{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bone_Names = ['Fil1', 'Fil2', 'Fil4', 'Fil5', 'Fil6', 'Fil8', 'Fil10', 'G24', 'G47', 'G49']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filament orientations relative to the galactic North (see filament orientation fit notebook)\n",
    "Theta_F = [93, 74, 96, -98, -93, 86, 95, -73, 70, 99]\n",
    "Theta_F_array = np.array(Theta_F)\n",
    "# Mean HAWC+ polarization angle for each filament (see histogram in each filament notebook)\n",
    "Theta_P = [29.3, 63.1, 44.8, -83.9, -84.7, 60.3, 25.8, -58.9, 65.2, 86.5]\n",
    "Theta_P_array = np.array(Theta_P)\n",
    "# Mean Planck polarization angle for each filament (see Planck statistics notebook)\n",
    "Theta_PlaP = [-5.9, -9.4, 3.6, 1.1, -12.1, -5.5, 6.4, -1.1, 26.0, -35.3]\n",
    "Theta_PlaP_array = np.array(Theta_PlaP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def circ_stat(array):\n",
    "    # Takes a numpy array in degrees\n",
    "    # Conversion from degrees to radians\n",
    "    conv_rad = math.pi/180.0\n",
    "    conv_deg = conv_rad**-1.0\n",
    "    # Circular mean of the array\n",
    "    circular_mean = conv_deg * stats.circmean(conv_rad*array, \n",
    "                                              low=-math.pi/2.0, \n",
    "                                              high=math.pi/2.0,\n",
    "                                              nan_policy='omit')\n",
    "    # Circular standard deviation of the array\n",
    "    circular_stdev = conv_deg * stats.circstd(conv_rad*array, \n",
    "                                              low=-math.pi/2.0,  \n",
    "                                              high=math.pi/2.0,\n",
    "                                              nan_policy='omit')\n",
    "    # Returning the values\n",
    "    return circular_mean, circular_stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert angles to -90 to 90 values\n",
    "def fix_angles90(OneDarray):\n",
    "    OneDarray2 = np.copy(OneDarray)\n",
    "    # Printing the input array\n",
    "    print('Input Array')\n",
    "    print(OneDarray)\n",
    "    # Checking each element that needs to be fixed\n",
    "    for i in range(0, np.shape(OneDarray)[0]):\n",
    "        if OneDarray[i] > 90.0:\n",
    "            OneDarray2[i] = OneDarray[i] - 180.0\n",
    "        if OneDarray[i] < -90.0:\n",
    "            OneDarray2[i] = OneDarray[i] + 180.0\n",
    "    # Printing the output array\n",
    "    print('Output Array')\n",
    "    print(OneDarray2)\n",
    "    return OneDarray2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Array\n",
      "[ 93  74  96 -98 -93  86  95 -73  70  99]\n",
      "Output Array\n",
      "[-87  74 -84  82  87  86 -85 -73  70 -81]\n",
      "Input Array\n",
      "[ 29.3  63.1  44.8 -83.9 -84.7  60.3  25.8 -58.9  65.2  86.5]\n",
      "Output Array\n",
      "[ 29.3  63.1  44.8 -83.9 -84.7  60.3  25.8 -58.9  65.2  86.5]\n",
      "Input Array\n",
      "[ -5.9  -9.4   3.6   1.1 -12.1  -5.5   6.4  -1.1  26.  -35.3]\n",
      "Output Array\n",
      "[ -5.9  -9.4   3.6   1.1 -12.1  -5.5   6.4  -1.1  26.  -35.3]\n",
      "Input Array\n",
      "[119.3 153.1 134.8   6.1   5.3 150.3 115.8  31.1 155.2 176.5]\n",
      "Output Array\n",
      "[-60.7 -26.9 -45.2   6.1   5.3 -29.7 -64.2  31.1 -24.8  -3.5]\n",
      "Input Array\n",
      "[ 84.1  80.6  93.6  91.1  77.9  84.5  96.4  88.9 116.   54.7]\n",
      "Output Array\n",
      "[ 84.1  80.6 -86.4 -88.9  77.9  84.5 -83.6  88.9 -64.   54.7]\n"
     ]
    }
   ],
   "source": [
    "# Fixing the angles\n",
    "Theta_F_np = fix_angles90(Theta_F_array)\n",
    "Theta_P_np = fix_angles90(Theta_P_array)\n",
    "Theta_PlaP_np = fix_angles90(Theta_PlaP_array)\n",
    "\n",
    "# Mean magnetic field angle for each filament \n",
    "Theta_B_array =  Theta_P_np + 90.0\n",
    "Theta_PlaB_array =  Theta_PlaP_np + 90.0\n",
    "\n",
    "# Fixing the angles\n",
    "Theta_B_np = fix_angles90(Theta_B_array)\n",
    "Theta_PlaB_np = fix_angles90(Theta_PlaB_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[177.  16. 174.   8.   3.   4. 175. 163.  20. 171.]\n",
      "[ -26.3  100.9  -38.8   75.9   81.7  115.7  -20.8 -104.1   94.8  -77.5]\n",
      "[-171.1   -6.6    2.4  170.9    9.1    1.5   -1.4 -161.9  134.  -135.7]\n",
      "[150.7 116.9 135.2  83.9  84.7 119.7 154.2  58.9 114.8  93.5]\n",
      "[  5.9   9.4 176.4 178.9  12.1   5.5 173.6   1.1 154.   35.3]\n",
      "\n",
      "Correcting difference between the filament orientations and the galactic plane\n",
      "Input Array\n",
      "[177.  16. 174.   8.   3.   4. 175. 163.  20. 171.]\n",
      "Output Array\n",
      "[ -3.  16.  -6.   8.   3.   4.  -5. -17.  20.  -9.]\n",
      "Correcting difference between the HAWC+ magnetic field and the filament orientations\n",
      "Input Array\n",
      "[ -26.3  100.9  -38.8   75.9   81.7  115.7  -20.8 -104.1   94.8  -77.5]\n",
      "Output Array\n",
      "[-26.3 -79.1 -38.8  75.9  81.7 -64.3 -20.8  75.9 -85.2 -77.5]\n",
      "Correcting difference between the  Planck mangetic field and the filament orientations\n",
      "Input Array\n",
      "[-171.1   -6.6    2.4  170.9    9.1    1.5   -1.4 -161.9  134.  -135.7]\n",
      "Output Array\n",
      "[  8.9  -6.6   2.4  -9.1   9.1   1.5  -1.4  18.1 -46.   44.3]\n",
      "Correcting difference between the HAWC+ magnetic field and the galactic plane\n",
      "Input Array\n",
      "[150.7 116.9 135.2  83.9  84.7 119.7 154.2  58.9 114.8  93.5]\n",
      "Output Array\n",
      "[-29.3 -63.1 -44.8  83.9  84.7 -60.3 -25.8  58.9 -65.2 -86.5]\n",
      "Correcting difference between the Planck mangetic field and the galactic plane\n",
      "Input Array\n",
      "[  5.9   9.4 176.4 178.9  12.1   5.5 173.6   1.1 154.   35.3]\n",
      "Output Array\n",
      "[  5.9   9.4  -3.6  -1.1  12.1   5.5  -6.4   1.1 -26.   35.3]\n"
     ]
    }
   ],
   "source": [
    "# Analysis using signed values\n",
    "\n",
    "# Difference between filament and galactic disk \n",
    "Diff_FG = 90.0-Theta_F_np\n",
    "print(Diff_FG)\n",
    "# Difference between filament and HAWC+ magnetic field\n",
    "Diff_FB = Theta_F_np-Theta_B_np\n",
    "print(Diff_FB)\n",
    "# Difference between filament and Planck magnetic field\n",
    "Diff_FPlaB = Theta_F_np-Theta_PlaB_np\n",
    "print(Diff_FPlaB)\n",
    "# Difference between galactic disk and HAWC+ magnetic field\n",
    "Diff_GB = 90.0-Theta_B_np\n",
    "print(Diff_GB)\n",
    "# Difference between galactic disk and Planck magnetic field\n",
    "Diff_GPlaB = 90.0-Theta_PlaB_np\n",
    "print(Diff_GPlaB)\n",
    "print('')\n",
    "\n",
    "print('Correcting difference between the filament orientations and the galactic plane')\n",
    "Diff_FG = fix_angles90(Diff_FG)\n",
    "print('Correcting difference between the HAWC+ magnetic field and the filament orientations')\n",
    "Diff_FB = fix_angles90(Diff_FB)\n",
    "print('Correcting difference between the  Planck mangetic field and the filament orientations')\n",
    "Diff_FPlaB = fix_angles90(Diff_FPlaB)\n",
    "print('Correcting difference between the HAWC+ magnetic field and the galactic plane')\n",
    "Diff_GB = fix_angles90(Diff_GB)\n",
    "print('Correcting difference between the Planck mangetic field and the galactic plane')\n",
    "Diff_GPlaB = fix_angles90(Diff_GPlaB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circular statistics for the filament orientation\n",
      "(88.95979798729783, 10.882957008944178)\n",
      "\n",
      "Statistics for the difference between filament and galactic disk\n",
      "(1.0402020127021518, 10.88295700894416)\n",
      "1.1 10.830050784737807\n",
      "\n",
      "===\n",
      "\n",
      "Circular statistics for the HAWC+ polarization orientation\n",
      "(67.89551626508413, 30.824551493458227)\n",
      "\n",
      "Circular statistics for the HAWC+ magnetic field orientation\n",
      "(-22.104483734915874, 30.824551493458227)\n",
      "\n",
      "Circular statistics for the difference between filament and HAWC+ magnetic field orientation\n",
      "(-73.66714142442437, 31.5203275296431)\n",
      "-15.85 64.78475515119278\n",
      "\n",
      "Circular statistics for the difference between galactic disk and HAWC+ magnetic field orientation\n",
      "(-67.89551626508413, 30.82455149345822)\n",
      "-14.750000000000004 61.958893631180985\n",
      "\n",
      "===\n",
      "\n",
      "Circular statistics for the Planck polarization orientation\n",
      "(-3.0582662113264614, 14.595320388330066)\n",
      "\n",
      "Circular statistics for the Planck magnetic field orientation\n",
      "(86.94173378867355, 14.59532038833006)\n",
      "\n",
      "Circular statistics for the difference between filament and Planck magnetic field orientation\n",
      "(2.8322691165528684, 20.951343020321968)\n",
      "2.120000000000002 21.585912072460594\n",
      "\n",
      "Circular statistics for the difference between galactic disk and Planck magnetic field orientation\n",
      "(3.0582662113264485, 14.59532038833006)\n",
      "3.22 14.746443639060912\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Circular statistics for the filament orientation')\n",
    "print(circ_stat(Theta_F_np))\n",
    "print('')\n",
    "print('Statistics for the difference between filament and galactic disk')\n",
    "print(circ_stat(Diff_FG))\n",
    "print(np.mean(Diff_FG),np.std(Diff_FG))\n",
    "print('')\n",
    "print('===')\n",
    "print('')\n",
    "print('Circular statistics for the HAWC+ polarization orientation')\n",
    "print(circ_stat(Theta_P_np))\n",
    "print('')\n",
    "print('Circular statistics for the HAWC+ magnetic field orientation')\n",
    "print(circ_stat(Theta_B_np))\n",
    "print('')\n",
    "print('Circular statistics for the difference between filament and HAWC+ magnetic field orientation')\n",
    "print(circ_stat(Diff_FB))\n",
    "print(np.mean(Diff_FB), np.std(Diff_FB))\n",
    "print('')\n",
    "print('Circular statistics for the difference between galactic disk and HAWC+ magnetic field orientation')\n",
    "print(circ_stat(Diff_GB))\n",
    "print(np.mean(Diff_GB), np.std(Diff_GB))\n",
    "print('')\n",
    "print('===')\n",
    "print('')\n",
    "print('Circular statistics for the Planck polarization orientation')\n",
    "print(circ_stat(Theta_PlaP_np))\n",
    "print('')\n",
    "print('Circular statistics for the Planck magnetic field orientation')\n",
    "print(circ_stat(Theta_PlaB_np))\n",
    "print('')\n",
    "print('Circular statistics for the difference between filament and Planck magnetic field orientation')\n",
    "print(circ_stat(Diff_FPlaB))\n",
    "print(np.mean(Diff_FPlaB),np.std(Diff_FPlaB))\n",
    "print('')\n",
    "print('Circular statistics for the difference between galactic disk and Planck magnetic field orientation')\n",
    "print(circ_stat(Diff_GPlaB))\n",
    "print(np.mean(Diff_GPlaB),np.std(Diff_GPlaB))\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert angles to 0 to 180 values\n",
    "def fix_angles180(OneDarray):\n",
    "    OneDarray2 = np.copy(OneDarray)\n",
    "    # Printing the input array\n",
    "    print('Input Array')\n",
    "    print(OneDarray)\n",
    "    # Checking each element that needs to be fixed\n",
    "    for i in range(0, np.shape(OneDarray)[0]):\n",
    "        if OneDarray[i] > 180.0:\n",
    "            OneDarray2[i] = OneDarray[i] - 180.0\n",
    "        if OneDarray[i] < 0.0:\n",
    "            OneDarray2[i] = OneDarray[i] + 180.0\n",
    "    # Printing the output array\n",
    "    print('Output Array')\n",
    "    print(OneDarray2)\n",
    "    return OneDarray2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Array\n",
      "[-60.7 -26.9 -45.2   6.1   5.3 -29.7 -64.2  31.1 -24.8  -3.5]\n",
      "Output Array\n",
      "[119.3 153.1 134.8   6.1   5.3 150.3 115.8  31.1 155.2 176.5]\n",
      "Input Array\n",
      "[ 84.1  80.6 -86.4 -88.9  77.9  84.5 -83.6  88.9 -64.   54.7]\n",
      "Output Array\n",
      "[ 84.1  80.6  93.6  91.1  77.9  84.5  96.4  88.9 116.   54.7]\n"
     ]
    }
   ],
   "source": [
    "# Changing from -90 to 90 to 0 to 180. \n",
    "Theta_B_180 = fix_angles180(Theta_B_np)\n",
    "Theta_PlaB_180 = fix_angles180(Theta_PlaB_np)\n",
    "# Uncertainties\n",
    "Uncertain_B = [36.8, 45.2,51.5,49.3,36.6,54.6,43.4,60.2,30.2,65.1]\n",
    "Uncertain_PlaB = [10.0,11.9,7.5,10.0,7.2, 4.6, 9.5,10.7,20.1,18.3] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7nZ0dxcXFlbg8me5+L++UhlHPy8sjAwMD2rZtm8j23Nxc0tPTo71795KHhwfp6OiI+DxOmDCBevbsWWiZJe2+Sk1NpZcvX4os1tbWNHz48N/OTk9LS6NKlSrRP//8U+j+jIwM4QPK19dXZnz+/4QsXZPC+PbtG2lra9P+/fuJSDA56OeXJR6PR9WqVROZKPcr//77L2lra5OpqSk9ffq0SPX+kVIYU5el6yKNe4WI6O3bt6ShoUHXr18vwjfCPLJ0TYhKdi/8iadPn5KpqSkBIAUFBdq4caNw3klJYY06g5SGUc9/479161aBh0S/fv3I2tqa4uLiSEVFhdauXUufPn2i1atXk6GhoXCimTT5dZxw7ty5dP/+fQoPD6fHjx+Tra0t6evrU0IhY6lcLpeaNm0qVf9gaSBr1+T69et07do1+vTpE928eZMsLS2pZcuWwrG8M2fOkLKyMh09epRev35NEydOJB0dnUJbE5mZmTR58mQCQIMGDaLv379LRmQpGHVZuy6/Is69cv/+fWGr/Fe3N1lG1q5Jce6FP8Hj8Wjjxo2koKBAACT38ksybtRnz55d6DJnzhxatGgRubu7l4lJHr+jNIx6r169/pqSLyQkhM6cOUM1atQgVVVV6tmzJ4WFhUlN08/8+qAaMmQIVa1alZSUlKhatWo0ZMiQAlr4fL7wbfbs2bN/DMQgi8jaNTl79izVrFmTlJSUyNDQkKZNm1YgEtfOnTvJ2NiYlJSUqEWLFoU+gF6/fk2NGjUiFRUV2r9/v2SjkJWCUZe16/IrJblXiIg+fPhAcnJydObMmVLRKUlk8ZoU5V74E3FxcWRnZyfUP2jQIIlGvpNpo96xY0fS0tIidXV1atq0KTVt2pQ0NDRIW1ubWrZsSTo6OlSpUqXfh5aUcWQtTGxZgMfj0eDBgwv4qbIwB5/Pp0OHDpGqqipZWFhIJ9BPKQefKQ9ERUUJX6z8/PzKRKjX8s6tW7fI0NBQOFv/wIEDEr8u0rQronlLS4CDgwNsbW0RExODgIAABAQEICoqCl27doWjoyOio6PRvn17zJ49W9yqWMoIcnJyaNq0KerWrcu0FJYfZGZmYs2aNRg+fDiePXuGhg0bSr4SdXWgVSvBmuWvREREoG7dusKU1S1atACHw2FYVcUlLy8PixcvRrdu3RAXF4cGDRrg+fPnmDBhQpm6LhwiInEKqFatGm7dugULCwuR7a9evUK3bt0QHR2NwMBAdOvWDV+/fhVLLBOkpqZCW1sbKSkpbG7iP0BE2LhxIwwNDTFq1Cim5bD84NmzZ6hSpQpMTEzw/ft3VKpUiWlJFZ68vDwoKioCAI4dO4aBAwcWcAVmKV2+fPmCYcOG4cmTJwCAiRMnYtu2bVBTU5NKfdK0K2K31FNSUpCQkFBge2JiIlJTUwEAOjo6yM3NFbcqFhmGw+EgLCwMX758YVoKyw+4XC6cnJywfv16AGANugyQ3wK8cuUKAGDUqFGsQWeY8+fPw8rKCk+ePIGWlhY8PT2xf/9+qRl0aSN28BkHBweMHTsWW7ZsQfPmzQEIWgfz5s1D3759AQD+/v6oU6eOuFWxyCC3bt0CEaFbt244cOBAmeqmKq8kJiYiNzcX1apVw7Vr11CjRo3SqTgwEGjWDAgIAJo2LZ06yxgGBgbo168f+zyUAbKysjB37lzs3bsXgGD448yZMzAzM2NYmZiIOyiflpZG48ePJyUlJZKTkyM5OTlSUlKiCRMmCLO1BQUFUVBQkLhVMQI7Ue7P9O7dm5ycnJiWwfKDO3fuUNWqValv376lXzk7Ua5QkpOTqX///hJzh2IRn9evX1Pjxo2Fs9tdXFyKHepVHGQ6TKyGhgYOHjyIbdu24dOnTwCAmjVrQkNDQ3iMlZWVuNWwyBCfP39GSkoKLC0t4eHhUWa7qcoTXC4XK1euxJo1a9C5c2fs2bOHaUksP1BTU0NOTo5wOJKFOYgIR48exfTp05GZmYkqVarg+PHjsLOzY1qaxBDbqAPAo0ePsH//fnz69Annzp2DhoYGTpw4ATMzM7Rt21YSVbDIEJMmTQKXy8WdO3dEXt5YmCEiIgLDhg3D06dP4ebmBldXV8jLyzMtq0LD4/Gwfv16DBo0CHXq1MHly5eZllThSU1NxeTJk3H69GkAgK2tLU6cOAFDQ0OGlUkWsSfKeXl5wc7ODqqqqggMDEROTg4AwQS6tWvXii2QRTbIyspCdHQ0AODw4cPw8vJiWBELAHh7e8PKygqRkZF48OABFi1axBp0GSArKwunTp3Co0ePmJbCAuD58+do2rQpTp8+DXl5eaxduxY3btwodwYdgPhj6lZWVnTs2DEiItLQ0KCPHz8SEVFgYCAZGBiIWzzjsGPqAvr160dt27Zlg2PICDwej6ZPn04AqF+/fvTt2zemJRFlZRF9+CBYV1AuX74sDEeaVYG/B1mBx+PRli1bSFFRkQCQsbExPX78mGlZsh185t27d2jfvn2B7dra2khOTha3eBYGISJkZmYCAJYtW4a9e/eys9tlBDk5OSgpKWH37t3w8vKSDXc1FRXA3FywroBkZGRg/PjxOHDgAAD8Nh0rS+mQmJiI3r17Y+7cucjLy0P//v0RHByM1q1bMy1Nqog9pm5oaIiwsDCYmpqKbPfx8UHNmjXFLZ6FQZydnfH+/Xtcu3aNnewoIxw7dgxEhNGjR2PLli1MyxElPBxYuhRYvRoo625BxSAsLAwGBgbQ1NTE06dPYWxszLSkCs/9+/fh5OSEmJgYKCsrY9u2bZg8eXKFaJSI3VKfMGECnJ2d4efnBw6Hg5iYGJw6dQrz5s3DlClTxCr74cOH6N27N4yMjMDhcHDhwoUCx7x58wZ9+vSBtrY21NXV0bx5c0RERAj3Z2dnY9q0adDT04OGhgYGDBiA+Ph4sXSVd+hHkMHevXtjzJgxFeJGKCv4+vrC39+faRmF8/07cOqUYF1ByM7ORvv27bF69WoAgImJCXu/MAiXy8WyZcvQuXNnxMTEoF69evD398eUKVMqznURt/+ez+eTm5sbqaurE4fDIQ6HQyoqKrRkyRKxxwauXr1KixcvpvPnzxOAAonvw8LCSFdXl+bPn0+BgYEUFhZG//33H8XHxwuPmTx5MtWoUYPu3LlDz58/p1atWlHr1q2LrKGijanv3LmTxo8fz7QMlp94/vw5eXp6EhEJU3PKJBXITz09PV3o1/zw4UNKTU1lWBFLREQEtWvXTuh7PnbsWEpPT2daVqHIdJa2fHJycujVq1fk5+cnDDojSQoz6kOGDKHhw4f/9pzk5GRSVFSkc+fOCbe9efOGAJCvr2+R6q1oRv348eM0f/58YdpUFubg8/m0bds2UlRUpHbt2sn+JMUKYtRzc3OpUaNGtGjRIqalsPzgv//+I11dXQJAmpqa5OHhwbSkPyLTE+XyUVJSgoWFBVq0aFEqvst8Ph9XrlxBnTp1YGdnhypVqqBly5YiXfQBAQHIy8uDra2tcFu9evVgbGwMX19fqWssK9y9excbN24EAIwYMQIbN26EnJzEfhosJeDr16/o06cPZs+ejenTp+PWrVsVp/tQRiFBIwiKiopwcXHByJEjmZZU4cnOzsbMmTPh4OCAb9++wdraGkFBQXB0dGRaGmOUaKLcnDlzinzs1q1bS1LFX0lISEB6ejrWr18PNzc3bNiwAdevX0f//v1x7949dOjQAXFxcVBSUoKOjo7IuQYGBoiLiyu03JycHKGvPYAKEQXq+fPnuH37NubMmQMFBYnEI2IRgwcPHmDYsGHIycnBpUuX0KtXL6YlFY2qVYHlywXrcgaPx8OgQYPQvn17zJo1C8OHD2daUoXn/fv3GDJkCIKDgwEAc+fOxdq1a6GkpMSssGLwq31RVlaGsrKyWGWW6AkeFBQk8n9gYCC4XK4wf/b79+8hLy+PZs2aiSXuT/D5fACChDL5udrzM+3s27cPHTp0KFG569atw8qVKyWmU1aJiIiAv78/Bg4ciHnz5mHu3Lls0BKG4fF4WL16NVavXo127drh1KlTqFatGtOyik7VqsCKFUyrkAry8vKwtLQs+8k+ygnHjx/H1KlTkZGRAX19fRw7dgw9evRgWlax+TXZ0vLly7FCzHuoREb93r17wr+3bt0KTU1NHDt2TOgr+/37d4wZMwbt2rUTS9yf0NfXh4KCQoE87vXr14ePjw8Agbtdbm4ukpOTRVrr8fHxv40ktHDhQpGeiNTU1NLLclWK7N27F15eXujTp0+ZerMtz4wYMQJnz57F8uXLsXjx4rL3kpWaCvj6AjY2gIRzRDMBn8/HunXrULt2bQwePBjLly9nWlKFJz09HVOnTsWJEycAAJ06dcLJkydhZGTEsLKSERkZKZJPXdxWOgDxZ78bGRlRaGhoge0vX76kqlWrilu8EBQyUc7GxqbARLm+ffuSo6MjEf1/oty///4r3P/27dsKO1EuKyuLnj17RkRE2dnZshGFjEVkFvWDBw8YViMG5WyiHJ/PJycnJ1q3bh3TUlhIEKW0du3aBIDk5ORo9erVsu0N8gdkOktbamoqEhMTC2xPTExEWlqaWGWnp6cjLCxM+H94eDiCg4Ohq6sLY2NjzJ8/H0OGDEH79u3RqVMnXL9+HZcuXcL9+/cBCKLajRs3DnPmzIGuri60tLQwY8YM2NjYoFWrVmJpK4usXLkS7u7u+Pz5M1RVVSXzVsgiFm5ubnjw4AGuX78u1Z4tlqJz7do1qKmpoUOHDjhx4gQ7QZFhiAg7d+7E/PnzkZubi+rVq8PDw4O9X36HuG8FI0aMIFNTU/Ly8qLIyEiKjIykf//9l8zMzGjkyJFilX3v3j2hz+HPy6hRo4THHD58mMzNzUlFRYUsLS3pwoULImVkZWXR1KlTqVKlSqSmpkb9+vWj2NjYImso6y11Pp8vjEWdnJxMISEhDCti+Znbt2/TP//8I/vuakWhHLTU+Xw+denShcaOHcu0FBYi+vr1K/Xu3Vv47HdwcKCvX78yLUtsZNpPPSMjg6ZMmULKysokJydHcnJypKSkRFOmTJFZx//iUNaN+oYNG8jIyEgqsQNYSsaJEydowIAB5S8WQBk26mFhYfTq1SsiEtzz5e7alEEePHhA1atXJwCkpKREO3fuLB8vvyTj3e9qamrYs2cPNm3ahI8fPwIAatWqBXV1dXGLZhEDLpcLBQUFDB8+HMbGxmzecxkgPT0d06dPx7FjxzB8+HDk5ORAVVWVaVmSQ1kZqFVLsC5jjBkzBlpaWrh8+bLIxCWW0ofH42HNmjVYuXIl+Hw+6tSpg7Nnz7L5J4oIh+hHoO9iEBERUaykBdHR0WXLNecnUlNToa2tjZSUlDJzs3t4eGDTpk3w8fFhX65khODgYAwZMgTR0dHYs2cPG7hEBsjMzERqaioMDQ3x+fNn4bwbFuaIjo6Gk5MTHjx4AAAYNWoUdu3aVe4aJdK0KyUKG9a8eXNMmjQJz549++0xKSkpOHjwIBo2bAgvL68SC2QpPk2aNEHXrl3ZQDIyABFh165daNWqFdTU1BAQEMAadBlhwIABGDVqFADA1NSUNegMc+XKFVhaWuLBgwdQV1fH8ePHcfTo0XJn0KVOSfrsv379SrNnzyZtbW0yMDCgHj160Pjx42n69Onk5ORETZo0ISUlJWrVqhVduXJFouMFpU1ZGVN/9OgRDRkyhPLy8piWwvKDpKQkcnBwIAA0Y8YMysrKYlqSdAkJIdLXF6xlFD6fL7wO/v7+9Pr1a4YVsWRnZ9Ps2bOFk+GaNGlC7969Y1qWVJG5MXU9PT1s3boVa9aswZUrV+Dj44MvX74gKysL+vr6cHJygp2dHRo2bCjB1w+WP8HhcJCUlITU1FTo6uoyLYcFAne1R48e4cKFC3BwcGBajvThcoGvXwVrGWXSpElITEzE+fPn0bx5c6blVHjCwsIwdOhQBAQEAACcnZ2xYcMG1t1WDEo0pl6RkOUx9aioKLi7u2Pp0qXgcDggItanlmF4PB7evHmDhg0bIj09Hd+/fy+XEQkLJTAQaNYMCAgAmjZlWk2hXLp0Cbm5uRgwYADTUio8Hh4emDRpEtLT06Grq4sjR46gT58+TMsqFWRuTJ1FNnj+/DkOHjyI2NhYAGANugywadMmtG3bFikpKdDQ0Kg4Bl2G2bp1K5ydnQEAvXv3Zg06w2RkZGDs2LFwcnJCeno62rVrh5CQkApj0KUNa9TLGDk5Ofj3338BAH379sW7d+/KbNzj8kR+VMVp06bh4sWL0NbWZlgRSz7q6upQV1cH2ynJPCEhIbC2tsaRI0cgJyeH5cuX4+7du6hevTrT0soNbPf7X5C17ncPDw+MGTMG7969g6mpKdNyKjy5ublYtGgRDh06hNDQ0Ir9cEpPB16+BBo1AhiesXzz5k28ffsWM2fOZFQHiwAiwt69ezFnzhzk5OTAyMgIp06dQseOHZmWxghs9zsLXr16BQBwdHTEq1evWIMuA3z8+BFt2rTBjh07sHz5crbHRENDkKFNBlyQHj9+jBs3bghTNLMwx/fv3zFgwABMmzYNOTk56NmzJ0JCQiqsQZc2rFEvA3h5ecHS0hLv3r0Dh8OBubk505IqPKdPn0aTJk3w/ft3PHnyBLNnz4acXAW/naKigDlzBGsG+Pz5My5evAgAWLZsGS5dusReE4Z5/PgxrKys4O3tDUVFRWzbtg2XLl2Cvr4+09LKLRL9xWdmZkqyuApPfpY7BwcHXLhwAXXr1mVYEUtGRgbGjRuHYcOGoVevXggMDIS1tTXTsmSDhARg2zbBmgG2bduGRYsWgcvlQl5enjXoDMLj8bB27Vp06NABERERqFWrFnx9fTFr1ix2Qq+Ukdivfvr06ahSpQqsrKwQFhaGqVOnYuDAgdi7d6+kqqhQ3L17FyYmJnj37h0UFBTQq1cvpiVVeF6+fInmzZvjzJkzcHd3x6lTp2RinkVFJisrC0FBQQCAdevWwcfHh42kyDCxsbGws7PD4sWLwePxMGzYMAQGBqJZs2ZMS6sQSMyoX7t2DV+/fsXevXvRtm1bVK9eHSNHjsTDhw+xdOlSSVVT7smft9iqVSvMnTuXHTuXISIiIqCoqIjnz59jzJgxbItDBli4cCH69OmD3NxcqKmpQUdHh2lJFZrr16/D0tISd+7cgZqaGo4cOYKTJ0+yL7+liaRC0zVp0kT4d506dYR/c7lcatasmaSqKXVKM0xscHAwNWnShKKioqReF0vR+P79O23cuFGY8pHL5TKsSIYppdSrfD6f4uPjiUgQsvrly5dSrY/l7+Tk5ND8+fOFoV4bN25Mb968YVqWzCJNuyKxlnpiYiIuXLiA8PBwkcxg8vLyrH9oEalWrRpq167NtAyWn/D19cW6deuEaYXl5eUZViTD6OsDU6cK1lJk9erVaNmyJbKysqCnp8eGo2aYT58+oV27dti0aRMAQawGPz8/1KtXj2FlFROJ+alv27YNoaGhCA0Nxbt372BhYYG6deuibt262LdvHz5//iyJakodafupx8bGYsGCBdixYwcbsERG4PP58PLywsCBA8HhcJCcnMx268oAPB4P8vLy+Pz5M/z9/TF48GCmJVV4zp49i4kTJyI1NRU6Ojpwd3dHv379mJYl80jTrkhsRsns2bNF/g8PDxca+TZt2kiqmnJHdnY2nj17hs+fP8PS0pJpORWeuLg4jBgxAnfu3MGTJ0/QqlUr1qAXlcxM4O1boF49QE1NokUfPXoUe/fuxcOHD2FqasrONWGYzMxMzJo1CwcPHgQAtG7dGh4eHjAxMWFYGYvEjPrt27exYsUK8Hg89OzZE66urjAzM0Pv3r0lVUW5ITc3F7t27cK0adNgZmaG0NBQ1v1GBrhx4wZGjhwJDoeDmzdvolWrVkxLKlu8fSu1hC6Wlpbo1KkTe5/IAKGhoRgyZAhev34NDoeDRYsWYcWKFazXgYwgsTtk2rRpmD9/Pnbu3Inw8HCsXLlSUkWXO969e4fly5fD19cXANgHFcPk5eXB1dUV9vb2aNKkCUJCQmBra8u0rArP/fv3MWLECPD5fDRp0gTr16+HoqIi07IqLESE/fv3o3nz5nj9+jUMDQ1x69YtuLm5sQZdhpCYNVFTU4ODgwOsra1x4MAB3L59W1JFlxtu374NPp+PRo0a4cuXL2yYRBkgPDwc7dq1w9atW7Fx40ZcvXoVBgYGTMtiAcDlchEfHy8MwsTCHMnJyRgyZAgmT56M7Oxs2NvbIyQkBF26dGFaGssvSHT2+7lz5/DixQtwuVzk5uZKquhywatXr9CtWzdcunQJAKCrq8uwIhYfHx9YWVkhPj4ePj4+mD9/PttrwjBfvnzB+vXrAQC2tra4ceMGO4GUYZ4+fYomTZrg3LlzUFBQwKZNm3DlyhVUqVKFaWkshVDkJ9jfXHnmzJmD69evY8KECahSpQpev36NwYMHY/Xq1bhw4UKJxD18+BC9e/eGkZEROBzOH8uZPHkyOBwOtm/fLrL927dvcHJygpaWFnR0dDBu3Dikp6eXSE9JiIyMBAA0aNAAz549Y3MGyxANGjTAyJEjERQUhJYtWzItp+wjJwdoagrWJeTJkyfYu3evMJUtG+CHOfh8PjZu3Ih27drh8+fPMDMzw+PHjzFv3jz25VeGKfKV+Zvn25w5c3D48GH4+fkhJSUFb968wYgRI6CgoCDM/11cMjIyYGlpid27d//xOG9vbzx9+rTQLFlOTk549eoVbt26hcuXL+Phw4eYOHFiifQUl7CwMNSuXRvnz58HADRr1ox9SDHMx48f0bFjR0RGRqJSpUrYuXMnO7tdUlhZAampgnUxyM7Oxn///QdAkIXw9evXqFy5suT1sRSZ+Ph4dO/eHa6uruByuRgyZAiCgoLQokULpqWx/I0iR6mRkxP+nZqaSnPmzKF69eqRvr4+1apVi7p3705r1qyht2/fSjpADpHgjYK8vb0LbI+KiqJq1apRaGgomZiY0LZt24T7Xr9+TQDo2bNnwm3Xrl0jDodD0dHRRaq3JJF/cnJyhH8fP36cMjMzi3wui3RJSkoie3t7qf1OWYrP4cOHSVVVtcj3JIt0uXnzJhkYGBAAUlVVpYMHDwojKrJIBpmLKDdy5EicO3cOw4YNw5o1azBjxgzcvXsXx48fh4WFBfr06YPo6GgJvnoUDp/Px4gRIzB//nw0aNCgwH5fX1/o6OiIZNGytbWFnJwc/Pz8pKIpKioK9erVw40bNwAAI0aMgKqqqlTqYikaKSkpmDhxIqKjo6Grq4tr166xGe+kwevXQIMGgvVfICK8/nHc6NGjERISwuajZ5i8vDwsXLgQdnZ2iI+PFw4Zjh8/nu1hLEOUyA/h5s2b8PHxQZMmTYTblixZgqtXr0JeXh5r1qxBixYt4OPjAzMzM4mJ/ZUNGzZAQUEBM2fOLHR/XFxcgckcCgoK0NXVRVxcXKHn5OTkICcnR/h/ampqsTQZGRlh0KBBbIhEGcHf3x9Dhw5FUlIShgwZgmrVqjEtqfySnS0w6NnZfz3Uw8MDY8aMwfv372FqasqGR2aYz58/Y9iwYUI328mTJ2Pr1q1sg0TK/GpflJWVoaysLFaZJWqpGxgY/DZ3uomJCQ4cOIApU6bA2dlZLHF/IiAgAP/88w+OHj0q0bfIdevWQVtbW7jUqFHjr+d8+/YNvXr1QkBAAOTk5LBhwwY2shLD8Pl8bN68GW3atEHlypURHBzMut/IAPmTVAcNGoTz58+zkeFkAC8vL1hZWcHX1xfa2trw9PTE3r17WYNeCtSoUUPE3qxbt07sMktk1KdPn46xY8ciJCTkt8cMHz4cd+/eLbGwv/Ho0SMkJCTA2NgYCgoKUFBQwJcvX0TSlRoaGiIhIUHkPC6Xi2/fvsHQ0LDQchcuXIiUlBThkj97/U+oq6uDw+EgIyND7M/FIj4JCQno2bMn5s+fjzlz5ki9x4ilaFy/fh1mZmYIDw+HkpISevXqxbSkCk1WVhamTJmCgQMHIiUlBS1btkRQUBAGDRrEtLQKQ2RkpIi9Wbhwodhllqj7fc6cOYiJiUHTpk3RtWtX9O3bF3w+X6TFfObMGehLMVvTiBEjCkT9srOzw4gRIzBmzBgAgI2NDZKTkxEQEIBmzZoBAO7evQs+n/9bF6aidn/k5eVh1apVGDNmDGrWrCn0P2dhljt37mD48OHg8/m4fv067OzsmJZU4SEicDgctG7dGs7OzuwQiAzw5s0bDBkyBC9fvgQALFiwAKtWrWIj9pUyWlpakk8UVuQZdT/Nfs/n6dOnNHDgQFJTUyMOh0OamppkZmZGurq6pKamRp6enmLN4ktLS6OgoCAKCgoiALR161YKCgqiL1++FHr8r7PfiYjs7e2pSZMm5OfnRz4+PlS7dm1ydHQssobfzVJMSUmhunXr0qlTp4r9uVikQ2hoKHE4HLK1taXY2Fim5VQ8vn8n+u8/wfoHz549o+bNm1NCQgJjslj+D5/PF3obAKAqVarQjRs3mJZV4ZDm7PciG3UOh/PbfTk5OeTr60vHjh2jnTt30unTpyk+Pl5scffu3SMABZZRo0YVenxhRj0pKYkcHR1JQ0ODtLS0aMyYMZSWllZkDb9++V5eXsIH1M+uayzMkZCQIHS5uXr1KvF4PIYVseQTExNDAwcOpJiYGKalVHhSUlLI0dFR+Bzt2rUr+/LLENI06hLLp15e+TnvLRHB3Nwcrq6umDdvHtPSWADExMSgYcOG2LlzJ5ycnJiWU7GJiwOOHEGUnR2W7dqFnTt3Ql1dnWlVLACePXuGoUOH4tOnT5CXl4ebmxtcXFzYyHAMUSbyqZd3MjIyULVqVQQGBqJ69epMy6nw5OXlQVFREUZGRli3bh169OjBtCSWmBhg0SKk16mDJ0+e4PPnz4XGj2ApPfh8PrZt24YFCxaAy+XCxMQEp0+fho2NDdPSfguXy8W1a9cAAN27d2czwBUT9jWtiOTHlK9RowYbiIFh3r59C2traxw7dgwAMGnSJFSqVIlhVRWbnJwcbDt1ClwA9czM8OrVK9agM0xiYiJ69eqFefPmgcvlYsCAAQgODpZpg14RSEhIAI/Hk1r5rFEvIrNnz2ZaQoWHiHD06FE0a9YMeXl5IsGPWJjl5cuXWLJnD579+P9vCaBYpMvdu3dhaWmJa9euQUVFBfv27cO5c+fYPAcMk5aWBisrK+zatUtqdbBGvYioqakxLaFCk5aWJnRXdHR0xLNnz9C4cWOmZVV47t69CyKCtbU1Iq5cAdsGZBYul4ulS5fC1tYWsbGxqF+/Pvz9/TFp0iS2h5FBuFwuiAiamprYvHkzHB0dpVaXRIz6o0ePMHz4cNjY2Ahjvp84cQI+Pj6SKJ6lghMQEICmTZvi4sWL8PDwwKFDh9gJWDJAYGAgunTpglu3bgEA9ExNgYEDAbY1yAgRERHo1KkT3NzcQEQYP348nj17hkaNGjEtrUKTlZWFdu3a4fDhwwCAYcOGSTUXvdhG3cvLC3Z2dlBVVUVQUJAwbnpKSgrWrl0rtkCWigsRYfv27bCxsYGOjg6CgoKk+obLUjSioqIAAE2bNoWfnx+6desm2FGzJnDunGDNUqpcuHABVlZW8PHxgaamJk6fPo2DBw+yL78ygKqqKuzs7Ert5Upso+7m5oZ9+/bh4MGDItGI2rRpg8DAQHGLZ6nAfP/+HRs3bsTMmTPx+PFj1KpVi2lJFZ5Xr16hVq1awtnJIvm1c3OBqCjBmqVUyM7OxowZM9CvXz98//4dzZs3R1BQEIYOHcq0tApNbm4uJk+eLIw0umLFit9GMZU0YvsKvHv3Du3bty+wXVtbG8nJyeIWz1IBefDgAerVqwcDAwO8fv2andwjA+Tm5kJJSQkWFhbYt28fOnfuXPCg0FCgWTMgIABo2rT0RVYw3r17hyFDhghzcMybNw9r1qyBkpISw8pYFBUVkZyczIgNFLulbmhoiLCwsALbfXx8UJPthmMpJtnZ2XB0dMQ///wDAKxBlwHCw8NRt25dPHz4EBwOB2PGjBE7PSSLeBw7dgzNmjVDSEgI9PX1cfXqVWzatIk16AxCRDh48CCePn0KDoeD06dPY8SIEaWuQ+yW+oQJE+Ds7Ax3d3dwOBzExMTA19cX8+bNw9KlSyWhkaUCEBkZCRUVFVSuXBkPHz5ks6rJEDVq1ED//v3ZayIDpKWlYerUqTh58iQAoHPnzjhx4gSMjIwYVsbC4/Hg7u4OOzs7tGrVijlvA3HjzPL5fHJzcyN1dXXicDjE4XBIRUWFlixZIm7RMoE0Y/SyCPjvv/9IV1eXxowZw7QUlh8kJCRQjx49KDQ0tOgnBQQQAYI1i8QJCAggc3NzAkBycnLk5uZGXC6XaVkSJy8vjy5evEgXL16kvLw8puX8FT8/P3r37h0REWVmZhbpHGnaFbG73zkcDhYvXoxv374hNDQUT58+RWJiIlavXi32CwdL+SYnJwfOzs5wcHBAu3btsHnzZqYlsfxAXV0dXC4XKSkpTEup8BAR/vnnH7Rq1QphYWGoUaMGHjx4gMWLF7NBfhiGz+dj3Lhx2LhxIwDBTHemkVhQ3fxJNCwsReH9+/cYOnQoXr16hZ07d2LatGlscAyGyc3NxcqVKzFlyhRUr14dN27cKF4BVlZAdjbA5uSWGF+/fsXYsWOFs6j79u2Lw4cPQ1dXl2FlFZuEhAQQEQwMDHDlyhVUrVqVaUlCSmTU58yZU+Rjt27dWpIqWMoxJ06cwJQpU1CtWjX4+fnBysqKaUksEIzXnjlzBk2aNMHAgQOLX4CcHMBOoJMYDx48gJOTE6Kjo6GkpIStW7di6tSp7MsvwxAR7O3tUbduXZw+fRrGxsZMSxKhREY9KCioSMexPz6Wn8nLy8P48eNx/PhxjBo1Crt27YKGhgbTsio8Xl5e6NKlC/T09PD69euSz2x//x6YOBE4cACoU0eyIisQPB4Pbm5uWLVqFfh8PurWrYszZ86wL78Mw+VykZubCzU1Nezbt0/mjHk+JTLq9+7dk7QOlgqAgoICVFRUcPz4cUZcPVgKkpSUhHHjxsHNzQ3Tp08Xz1UtPR148ECwZikRUVFRGD58OB48eAAAGD16NHbu3Mm+/DIMEaF3794wMDDA0aNHRYMuyRhsoloWqUJE2LVrF6pWrYqBAwdi//79TEtiAfD69WvUrFkTenp6CAkJkdlWR0Xi8uXLGD16NJKSkqChoYG9e/di+PDhTMuq8BAROBwOJk6cCAMDA6bl/BWxjfrvxtc5HA5UVFRgbm4OBwcHdmJHBebJkyeoVatWycZpWSROSkoKWrduDVdXVyxcuBAmJiZMS6rQ5OTkYMGCBdi+fTsAQUz9M2fOoHbt2swKY8Hs2bOhqKiIjRs3ol+/fkzLKRJiG/WgoCAEBgaCx+Ohbt26AAQzm+Xl5VGvXj3s2bMHc+fOhY+PDzs7vgLx6NEjpKSkoFevXjh58iTreiMDpKSkQFNTE9ra2rhw4QJatWrFtKQKz4cPHzB06FBhnoxZs2Zh/fr1bMQ+GcHMzEwkp0lZQGw/dQcHB9ja2iImJgYBAQEICAhAVFQUunbtCkdHR0RHR6N9+/aYPXu2JPSyyDg8Hg+rV69Gx44dceDAAQBgDboMkJGRgSZNmmDLli0AgI4dO0JFRUWylRgbAwcPCtYsf+XUqVNo2rQpAgMDoaenh4sXL2Lbtm2sQWeYo0ePYvfu3QCAmTNnYsqUKQwrKibiRq8xMjKiV69eFdgeGhpKRkZGRCSIhKSnpyduVYzARpQrOtHR0dSxY0ficDi0bNmyMhENqrzD4/GIz+cTEdGhQ4fo8+fPDCtiSUtLo9GjRxMAAkDt27enyMhIpmX9ltjYWJo5cybVqlWLlJWVqUqVKtS6dWvas2cPZWRkiBzL5/PJ3t6eAJC3t7dw+5EjR4Sf99clPj5epAymI8rNnTuXJk+eLNU6pGlXxO5+T0lJQUJCQoGu9cTERKSmpgIQJOXIZdMxlmuuXLmC0aNHQ0lJCXfv3kXHjh2ZllThyc7ORu/eveHk5ITRo0dj3Lhx0q3w61fgwgWgb19AX1+6dZVRgoODMXToULx79w5ycnJYtmwZlixZIrO9WZ8+fUKbNm2go6ODtWvXolGjRlBWVsbLly9x4MABVKtWDX369BEev3379kJdmYcMGQJ7e3uRbaNHj0Z2djaqVKki9c/xN54/f46YmBj06dMHGzduhJyc2J3YzCHuW8GwYcPIzMyMzp8/T5GRkRQZGUnnz5+nmjVr0vDhw4mI6PTp09SsWbNil/3gwQPq1asXVa1atcCbX25uLrm4uFDDhg1JTU2NqlatSiNGjKDo6GiRMpKSkmjYsGGkqalJ2traNHbsWEpLSyuyBral/mdycnJozpw5BIB69uxJiYmJTEti+QkXFxe6fft26VTGxn7/LXw+n3bt2kXKysoEgKpVq0b3799nWtZfsbOzo+rVq1N6enqh+/N7gYiIgoKCqFq1ahQbG1vgef0rCQkJpKioSMePHy+wj4mW+siRI6lz584in0eaSNOuiG3U09LSaPz48aSkpERycnIkJydHSkpKNGHCBOEPISgoiIKCgopd9tWrV2nx4sV0/vz5Aj+S5ORksrW1pbNnz9Lbt2/J19eXWrRoUeDlwd7eniwtLenp06f06NEjMjc3J0dHxyJrYI36n7GzsyNFRUXaunVrqd0QLL8nLy+PXFxc6OrVq6VfOWvUCyUpKYn69u0r7G7u1atXmXj5/fr1K3E4HFq3bt1fj83IyKD69evThQsXiIj+atQ3b95M2trahSZAKS2jnpiYSM+fPycigR3Lzs6WWl2/ItNGPZ+0tDQKCQmhkJCQYrWEi8rffiRERP7+/gSAvnz5QkREr1+/JgD07Nkz4THXrl0jDodToEX/O1ijXji5ublERHTjxg3hjcHCPDwejxwcHGjPnj2lXzlr1Avg4+NDNWrUIACkqKhI27dvLzMvv0+fPiUAdP78eZHtenp6pK6uTurq6uTi4kJERBMnTqRx48YJj/nb87p+/fo0ZcqUQveVllF3cnKiRo0aEY/Hk1odv0Omx9Tz0dDQQOPGjSVVXIlISUkBh8OBjo4OAMDX1xc6OjqwtrYWHmNraws5OTn4+fmVGb9DWWPu3LkIDw+Hl5cXunXrxrQcFgDnzp2DmZkZrK2t4e3tzYZoZhgej4f169dj+fLl4PF4MDc3x5kzZ9CsWTOmpYmNv78/+Hw+nJyckJOTg4sXL+Lu3btFDh/u6+uLN2/e4MSJE1JWWhAul4v4+HhUq1YNmzdvBo/HK9vj54UgEaN+584d3LlzBwkJCeDz+SL73N3dJVHFX8nOzoarqyscHR2hpaUFAIiLiyswCUNBQQG6urqIi4srtJycnBzk5OQI/8+f7Mfyf9q3b49GjRoxLYPlBzweD5s2bUKHDh1gbW3NnEHX0AA6dBCsKzAxMTEYMWIE7t69CwAYPnw49uzZA01NTYaVFQ9zc3NwOBy8e/dOZHvNmjUB/D/N6N27d/Hx40dhYyqfAQMGoF27drh//77I9kOHDsHKyoqRF5wZM2bg0aNHCAkJgaGhYanX/yu/2hdlZWWxXRrFfkVZuXIlunXrhjt37uDr16/4/v27yFIa5OXlYfDgwSAi7N27V6yy1q1bB21tbeFSo0YNCaksu+R/ryNHjgQRwcHBAaNHj2Zbgwzz6tUrfPr0CfLy8rh9+7YwpzNj1KkD3L9foZO5XLt2DVZWVrh79y7U1NRw9OhRnDhxoswZdADQ09ND165dsWvXLmRkZPz2uAULFuDFixcIDg4WLgCwbds2HDlyROTY9PR0eHp6St8T4xfyG2qzZs3C3r17ZcbboEaNGiL2Zt26deIXKm7/vaGhYaEzGCUNfjNGk5ubS3379qXGjRvT169fRfYdPnyYdHR0RLbl5eWRvLx8gXGifLKzsyklJUW4REZGVugx9W/fvlH//v0JAE2bNo31PZcReDweNWrUSOhhIhPweETZ2YJ1BSMnJ4fmzZsnnAxnaWlJb9++ZVqW2ISFhZGBgQHVq1ePzpw5Q69fv6a3b9/SiRMnyMDAgObMmVPoeb97Xh86dIhUVFTo+/fvv61T0mPqa9asodatW8vUsyt/TD0yMlLE3khisp7Y3e+5ublo3bq1uMWUiPwW+ocPH3Dv3j3o6emJ7LexsUFycjICAgKEXT13794Fn89Hy5YtCy1TEt0f5YUnT57A0dERqampOH/+PDsHQQZISUlBXl4e9PX1cf78eRgZGTEt6f8EBwPNmgEBAUDTpkyrKTU+ffqEoUOH4tmzZwCA6dOnY9OmTZKP2McAtWrVQlBQENauXYuFCxciKioKysrKsLCwwLx58zB16tRilXf48GH079+/QFe9NOnUqRM0NTVlpnX+M1paWsLhYokh7luBi4sLrVq1Suy3i8JIS0sTusMBoK1bt1JQUBB9+fKFcnNzqU+fPlS9enUKDg6m2NhY4ZKTkyMsw97enpo0aUJ+fn7k4+NDtWvXZl3a/gKPx6O1a9eSvLw8tW7dmo1CJiPw+Xxq2bIlDR48mGkphVMBZ7+fOXOGNDU1CQBVqlTprx46LH/H09OTTE1NSVFRkRo1akReXl7FLuPMmTM0fPhwmfU0kOnZ79nZ2Thw4ABu376Nxo0bFwh+v3Xr1hKX/fz5c3Tq1En4f35GuFGjRmHFihW4ePEiAMDKykrkvHv37gkjmp06dQrTp09Hly5dICcnhwEDBmDHjh0l1lTeiYuLw4gRI3Dnzh0sWrQIK1asgIICm6GXSfh8PnJzc6GiooLNmzejWrVqTEuq8GRmZsLZ2RmHDh0CALRt2xanTp0SK4Utl8uVlLwyi7e3NwYPHgwOhwMiQmhoKAYMGABPT89i9RQqKSkBEFwnWex5lea15hARiVPAz0a3QOEcjnAGaFklNTUV2traSElJkXw3iQwyZswYXL9+HSdPnkSXLl2YlsMCYODAgdDS0io1T5ISExhYIbrfX758iSFDhuDNmzfgcDhYvHgxli9fLvbL76VLlySksOwyc+ZMfPnyBT+bJQ6HA1NTU/zzzz9/PPfjx48ICAjA4MGDpS1TbDIzMzF06FCp2BWxm2D37t377b7Q0FBxi2cpBfLy8vD+/Xs0aNAAW7ZswYYNG2QiHjOLgEGDBqFSpUpMy6jwEBEOHDiAWbNmITs7G1WrVsXJkyfRuXNnpqWVG6Kjo/FrO5OIEBUV9ddzP3z4gCdPnqBPnz7lYj5DSRG7pf4raWlpOH36NA4fPoznz5+Dx+NJsvhSpyK01F1dXXHixAl8+vSpQt8MskR+0BI3NzempRSd3FwgIQGoUgX40f1ZXkhOTsb48ePh5eUFAOjevTuOHTuGypUrS6wOtvsdaNq0KUJDQwu01Bs1aoSAgIACx3/79g13797FwIEDQUTIy8sTdr3LMqmpqdDT05PNlno+Dx8+xOHDh+Hl5QUjIyP0798fu3btklTxLFIgISEBVapUwfz58zFw4EDWoMsQmpqaZe+FWEkJqF6daRUS5+nTpxg6dCi+fPkCRUVFrF+/HrNmzZJ4JDJ27gqwYsUKDBgwQDimnr/+3dyeY8eOYdOmTejRowe0tLQKzOmSVaR6rcWZZRcbG0vr1q0jc3NzqlKlCk2fPp0UFBQKza9eVimPs98zMjJowoQJVLlyZUpKSmJaDssPvLy86MCBA0zLKDkfPxINHChYlwN4PB6tX7+e5OXlCQDVrFmT/P39mZZV7vH09CQzMzNSVFSkxo0bF4gpwuVyKeCHhwWXyy1yHg9ZQiYTuvTq1Yu0tLTI0dGRLl++TFwul4iINeoyTmhoKDVo0IBUVVXp4MGDMuvyURGZOXMmDRs2rOxek3Lk0hYbG0tdu3YVBpMZOnRouXkGyDp/Cz6zdetWUldXLxBsrCwhky5t165dw8yZMzFlyhTUrl1bIr0GLNKDiHDw4EE4OzujVq1aePbsGRo0aMC0rArPmzdvEBERATs7O2zZsgXy8vJs+F2GuXnzJkaMGIGEhASoqqpi586dGDt2LHtdGCYpKQl6enqYPHkyWrZsWSDYGIuAEg8K+fj4IC0tDc2aNUPLli2xa9cufP36VZLaWCREcnIyhgwZgkmTJmHUqFGsQZch1q1bh5UrV4KIoKCgwBoOBsnLy8OCBQtgZ2eHhIQENGrUCM+fP8e4cePY68Iwx44dQ926dREfHw9VVVXGopiWBUrcUm/VqhVatWqF7du34+zZs3B3d8ecOXPA5/Nx69Yt1KhRo0wmMShv+Pn5YejQofj+/Ts8PT0xaNAgpiVVeFJTU/Hlyxc0atQIu3btYlvnMsDnz5/h6OiIp0+fAgCmTJmCLVu2CDORsTBDftbPXr16ISsri3W1LQJiT99UV1fH2LFj4ePjg5cvX2Lu3LlYv349qlSpgj59+khCI4sYfPr0CQYGBggKCmINuowwY8YMDBkyBHw+H1paWlBXV2dakmQwMgLWrhWsyxD//vsvrKys8PTpU2hra+Pff//Fnj17WIPOMIGBgbC2tkZycrKw2519+f07EvdTBwT5nS9dugR3d3dhKNeySln0U4+Pj4eHhwdmz54NQHA9ZDGZQUWCiPD161dUrlwZ0dHRyM7ORq1atZiWVaHJysrC7NmzsX//fgCCBFAeHh4wNTVlVlgFh8vl4tq1a4iPj4efnx+2bt1a7np9pWlXJOto+QN5eXn07du3zBv0ssrdu3exfv16xMbGAgBr0GWAOXPmoFOnTuByuahWrVr5NOjJycDFi4K1jPP69Wu0aNEC+/fvB4fDwcKFC/HgwQPWoDNMaGgonJyckJubCwMDA+zdu7fcGXRpIxWjzlL65OXlCaNdOTo64v3796hatSrDqljyA8hMmDABGzduLN8BRj59AhwcBGsZhYhw6NAhWFtbIzQ0FAYGBrhx4wbWrl1bZgKXlGe4XC7Cw8ORmprKtJQyC2vUywFfvnxBhw4dhEkmAEBbW5thVSz//PMPbG1tweVyYWFhgR49ejAtqUKTkpICR0dHTJgwAVlZWejWrRtCQkLQtWtXpqUxTseOHTFr1izh/6ampti+fXup1P39+3e4ubmBz+fDysoKjx8/hr6+fqnUXR5hjXoZx8vLC1ZWVoiJicGjR49Qv359piWx/KBp06bo3LkzO7lHBvD390eTJk1w9uxZKCgoYMOGDbh27RoMDAyYllaqjB49GhwOp8CyceNGrF69+q/nExG6d+8ODoeDCxcuSERTcHAwtm/fjvfv3wMAe7+ICWvUyyhZWVmYOnUqBg4ciC5duiA4OBg2NjZMy6rwXLlyBRMnTgQRoV27dli6dCk7p4FB+Hw+Nm/ejDZt2iA8PBympqZ49OgRXFxcJB67/U8w2RL+FXt7e8TGxooszZo1K9LY9fbt2yVidPl8Pv777z8QETp16oTw8HDUq1dP7HJZWKNeJnnz5g1atmwJd3d37N27F+fOnYOOjg7TslgAZGdnIyEhAdnZ2UxLKX1UVAALC8FaBkhISEDPnj0xf/58cLlcDBw4EEFBQWjVqpXU6ixpS/jAgQPo2LEjtLS0wOFwkFzIZMNv377ByckJWlpa0NHRwbhx45Cenl5sjcrKyjA0NBRZunTpIvLSURjBwcHYsmUL3N3di13nr9y5cwf9+vVDUFAQALCT4SRIiWbtzJkzp8jHbt26tSRVsPyGa9euYeDAgTAxMcGzZ8/QqFEjpiVVeN6/f4/Lly9jzpw5GDBgAPr3718xuxAtLIBXr5hWAUDgATJ8+HDExsZCRUUF//zzDyZMmFAq18Xe3h5HjhwR2Va5cuU/9thkZmbC3t4e9vb2WLhwYaHHODk5ITY2Frdu3UJeXh7GjBmDiRMnwsPDQ6L6f6dv2LBh2L17NwwNDUtczocPH1C7dm107doVoaGhsLCwkKBKFqCERj3/7SqfwMBAcLlc1K1bF4DgIScvL49mzZqJr5BFhCZNmmDixIlwc3MrP0FLyji3b9/GgQMHMHHiRGhoaFRMgy4jcLlcrFixAmvXrgURwcLCAmfPnkXDhg0lVv61a9cACHKqF+bNkN8S/pmOHTvCysrqt13u+a3k+/fvF7r/zZs3uH79Op49ewZra2sAwM6dO9GjRw9s3rwZRsUI+HP58mVoaGgI/+/evftfz5k9ezZat24NBweHItfzK48ePULHjh3x+PFjtGrVijXoUqJE3e/37t0TLr1790aHDh0QFRWFwMBABAYGIjIyEp06dULPnj0lrbdCEhoaig4dOiAxMRGGhobYtm0ba9AZJj09HZcvXwYgCCkaGBgo8qCskAQHA1pagjUDREREoGPHjlizZg2ICBMmTMCzZ88kZtCZxNfXFzo6OkKDDgC2traQk5ODn59fscrq1KkTgoODhcuOHTv+ePzFixdx9+7dEs8ByMzMBAC0adMGZ86cQYsWLUpUDkvREHtMfcuWLVi3bh0qVaok3FapUiW4ublhy5Yt4hbPAkBXVxfq6urIyspiWgrLD/bv348RI0bg+/fv4HA4UFNTY1oS8/D5QFqaYF3KeHt7C92htLS0cObMGRw4cICR65LfEs5fJBGeOS4urkDccwUFBejq6iIuLq5YZamrq8Pc3Fy4/C2exd27d/Hx40fo6OhAQUFB2DsxYMAAdOzY8Y/nBgUFwcTEBM+fP4ecnBwGDRpUqhMUKyJif7upqalITEwssD0xMRFpaWniFl9hSUxMxNixY5GUlAQjIyNcvXoVxsbGTMuq0BARXr9+DQBwdnZGQECAyMssS+mTnZ2N6dOno3///vj+/TtatGiBoKAgDBkyhDFNxW0JyzoLFizAixcvRD4TAGzbtq3A3IF88qOPN2jQAJMnT2bTc5ciYhv1fv36YcyYMTh//jyioqIQFRUFLy8vjBs3Dv3795eExgrHvXv3YGlpiUuXLiEsLIxpOSw/2L17N1q0aIHExEQoKCigZs2aTEuq0Lx9+xYtW7bE7t27AQDz58/Ho0ePGL8uxW0JFwVDQ0MkJCSIbONyufj27ZtYE9eKWnfDhg1FFgAwNjaGmZlZgePDw8PRpk0bhIWFQUlJCatXr2aDYZUiYhv1ffv2oXv37hg2bBhMTExgbGyMYcOGwd7eHnv27BGr7IcPH6J3794wMjIqNNgBEWHZsmWoWrUqVFVVYWtriw8fPogcIyk3kNKAy+Vi2bJl6NKlC+rVq4eQkBC0bNmSaVkVnvwep/yX18qVKzOsqGJDRDh69CiaNWuGFy9eoHLlyrh27Ro2btwIJSUlpuVJBRsbGyQnJyMgIEC47e7du+Dz+TL3jNDX10eVKlXA5XKZllIxIQmRnp5OISEhFBISQunp6RIp8+rVq7R48WI6f/48ASBvb2+R/evXrydtbW26cOEChYSEUJ8+fcjMzIyysrKEx9jb25OlpSU9ffqUHj16RObm5uTo6FhkDSkpKQSAUlJSJPKZfkdERAS1a9eO5OTkaPXq1cTlcqVaH0vROHPmDBkaGlJcXBzTUmSfjAyigADBWkqkpqaSk5MTASAA1LlzZ4qJiZFafb+Sl5dHFy9epIsXL1JeXl6B/aNGjSIHB4cC2zt06EDOzs7C/01MTGjbtm3C/2NjYykoKIgOHjxIAOjhw4cUFBRESUlJwmPs7e2pSZMm5OfnRz4+PlS7du1iPcukSXJyMk2ePJni4+PFLutv33F5QJp2Rab91Lt37/5bdwsiwvbt27FkyRKhm8Xx48dhYGCACxcuYOjQoRJ1A5EmFy9exJgxY6Curo4HDx6gbdu2TEuq8BAROBwOOnfujFmzZkFPT49pSbKPmhrQtKnUig8ICMDQoUMRFhYGeXl5rFq1Cq6uruUiYt++ffuwcuVK4f/t27cHABw5cgSjR48GAJw6dQrTp09Hly5dICcnhwEDBsjMeH1WVhbu3r2LoUOHFpjQx1K6SMRP/XdI0183PDwccXFxsLW1FW7T1tZGy5Yt4evri6FDh/7VDaRfv35S0VYUX9Z8njx5AgcHB/Tt2xeHDx+Grq6uVDSxFJ0HDx7A1dUVN2/eROXKleHq6sq0pLJBRASwYQPg6gpIcFInEeGff/6Bi4sL8vLyYGxsDA8PD7Rp00ZidUiKo0ePFrr9V//zz58/i/y/YsUKrFix4o9l6+rqFivQTHGeQyWBz+fjwIEDGD58OAwNDfH69ety8YJV1inRVb53756kdRSbfDeOXxMyGBgYCPeVxA0kJycHOTk5wv+llQIwISEBVapUgY2NDa5evQp7e3s2aImMUKNGDZiZmbFjgsXl61dgzx5g3DiJGfWvX79izJgxwpgA/fr1w6FDh9iXXxkgMjISLi4u0NfXx8CBA1mDXgJ+tS/KyspQVlYWq0zWYfAX1q1bB21tbeFSo0YNidfx/v171KxZE1euXAGHwxFmPfoVWUoCUVzi4uLg7OwMc3NzqKiowMDAAG3atMHevXuRmZmJb9++YcaMGahbty5UVVVhbGyMmTNnIiUlpdDykpKSUL169d/GxRaXsLAwjBs3Drm5uahZsyZOnz7NGg6GefDgASwtLXH58mUoKytj9+7d8PLyYq8Lwzx58gR5eXkwMTFBWFgYBg4cyLSkMkuNGjVE7M26devELlMiRv3Ro0cYPnw4bGxsEB0dDQA4ceIEfHx8JFF8oeS7ccTHx4tsj4+PF+4riRvIwoULkZKSIlwiIyMlpjk3NxcAULt2bWzcuBEdOnQAUPIkEB07dixwzuTJkyWm93dwuVxcunQJly5dKrQ1++nTJzRp0gQ3b97E2rVrERQUBF9fX7i4uODy5cu4ffs2YmJiEBMTg82bNyM0NBRHjx7F9evXMW7cuELrHDduHBo3biy1z5SSkgI/Pz/h75eFOfJDvXbu3BkxMTGoW7cu/Pz8MHXqVLY3i2Gio6PRsWNHHDt2DADY8XMxiYyMFLE3v4v7XxzEHmTx8vLCiBEj4OTkhKCgIGHXdUpKCtauXYurV6+KLbIwzMzMYGhoiDt37sDKygqAoCvDz88PU6ZMASDqBpIfh/5vbiCS6P4ojKCgIAwdOhQbN26Eg4MDpk6dKrK/JEkgAGDChAlYtWqV8H9ZiGw2depUKCgo4Pnz5yLhbGvWrAkHBwfhJDQvLy/hvlq1amHNmjUYPnw4uFyuyPjf3r17kZycjGXLlgnHCCVBRkYGDhw4AGdnZ6F7FBvtilmioqLg5OSEhw8fAhC4Ee7cuZMNi8wwcXFxMDAwQLVq1fDw4UM21KuE0NLSgpaWlkTLFPsJ5ubmhn379uHgwYNQVFQUbm/Tpg0CAwPFKjs9PV0kglF4eDiCg4MREREBDoeDWbNmwc3NDRcvXsTLly8xcuRIGBkZoW/fvgCA+vXrw97eHhMmTIC/vz8eP36M6dOnY+jQoaU2852IsGPHDrRq1QoaGhq/TWJQ0nSIampqIudI+gdSXJKSknDz5k1Mmzbttw/i37W2UlJSoKWlJWLQX79+jVWrVuH48eMSN7j+/v5YtmyZMEoca9DFpEoVYPZswboEXLp0CZaWlnj48CE0NDRw8uRJuLu7y5RB9/b2xsyZMzFgwAA0bdoU58+fZ1qS1ElISICFhQUOHToEAGjVqhV7r8gwYrfU3717J3S/+BltbW2xxz6fP3+OTp06Cf/Pd6UbNWoUjh49ChcXF2RkZGDixIlITk5G27Ztcf36daj8lM+ZSTeQ1NRU9O/fH5cvX8asWbOwfv16ifcCnDp1CidPnoShoSF69+6NpUuXMtpaDwsLAxEJM/blo6+vL8wxPm3aNGzYsEFk/9evX7F69WpMnDhRuC0nJweOjo7YtGkTjI2N8enTJ7H1ERHu3r2LLl26oFOnTvj8+XOZcleT6cl7hobAxo2Cv4uhMycnBwsXLsTOnTsBAE2bNsWpU6dgbm4uU5/X29sbgwcPBofDAREhNDQUAwYMgKenp9Q8acSBy+UKw7WW5HvM7zHT1dXFli1b0Lt371K5HsXRLekZ/eUBsb8RQ0NDhIWFwdTUVGS7j4+P2OEaO3bsKLy4hcHhcLBq1SqR7udfKa4biKQIDQ0VJrS5dOkSevXq9cfjS5IOMT+Kn5GREV68eAFXV1e8e/dOJlsP/v7+4PP5cHJyEvEuAAQvPz179oSFhYWIW8/ChQtRv359DB8+XGI67t69C1tbW/j7+6N58+ZlyqADkOjwg6SRz8qC1pcvSDUxAU9VtUjnxMTEYOPGjcIXNgcHB4wcORIfPnwoEB2SaVxcXIQGHfh/LANXV1eRhoSswOfzERUVBQC4ceNGsVrX6enpWLhwIYYMGYK2bdtCX18fvr6+0pIqQnF09+7du1Q0lSXENuoTJkyAs7Mz3N3dweFwEBMTA19fX8ybNw9Lly6VhMYyBY/Hw+rVq+Hm5ob69evj8uXLBV54CqNTp07Yu3ev8H91dXU4Ojr+8ZyfW7WNGjVC1apV0aVLF3z8+BG1atUq8WcQB3Nzc3A4HLx7905ke/4LnuovD/u0tDTY29tDU1MT3t7eIkM4d+/excuXL/Hvv/8C+H+SCH19fSxevFgkWMffiIyMRI0aNdClSxehQWeRLBoxMWjr4oKH27YhpQi/v3v37mHfvn3IysqCpqYmZs2aJdPXJTo6ukAjg4iEBqg8oa6uDmtra1SrVo1pKSzFRGyjvmDBAvD5fHTp0gWZmZlo3749lJWVMW/ePMyYMUMSGssUcXFx2L17N4YMGYLBgwejevXqRTovPwmEOORP/gsLC2PMqOvp6aFr167YtWsXZsyY8cfx0NTUVNjZ2UFZWRkXL14s0Nrx8vISSTf77NkzjB07Fo8ePSrW5/P19UWHDh1w//59tG7dWqYNx98oSg8OY/yYQ9O6des/RpZLT0/HzJkzceLECQCC6GnHjx+XeQNSr149hIaGihh2DoeD+vXry+R1+ffff7F582ZER0ejbt26WLZs2R+HCTIzMzFlyhSMGzcO7du3R48ePUpRLYukENuoczgcLF68GPPnz0dYWBjS09NhYWEh0pVcEbhx4wZatmyJatWq4e3bt6XWVfUz+RMKJZEVShz27NmDNm3awNraGitWrEDjxo0hJyeHZ8+e4e3bt2jWrBlSU1PRrVs3ZGZm4uTJk0hNTRUGYsif9f+r4f769SsAwQRIHR2dv+rIzc2FkpISWrZsif3795eLGbulMYYozXHT4OBgODk54f3795CTk8PSpUuxcOHCMhG4ZOnSpSJj6vlrWeyR9Pb2hqOjo1Djq1evMHjw4D+O/ysrKyMtLQ3fv38vZbUlR5bmXBQHaeqW2BNCSUnptzO7yzvJyckYOnQoFi1ahPnz5xfJ4IjLx48f4eHhgR49ekBPTw8vXrzA7Nmz0b59e6n6cxeFWrVqISgoCGvXrsXChQsRFRUFZWVlWFhYYN68eZg6dSr8/f3h5+cHAAV6KMLDw4s0ZPEnQkND0b17d1y8eBFNmjTBmDFjxCqvIlHScXvtjx/RHoLgJCm/xI8gIly5cgXu7u7gcrnQ19fHnDlz0LBhQ9y8eVMCqqWPiooKXF1dcfLkScTHx6N69epwdHSEsrKyzM11KOr4P5/Px6VLl2BlZQUTExNMmDABgGzP3SgPZGZmSq3sEid0Wb16NdTV1f+a3EWchC6yTlhYGKpUqQIdHR34+/uL3X1eHJSUlHD79m1s374dGRkZqFGjBgYMGIAlS5aUmoY/UbVqVezcuVM4o/lX/jYJUtxzateujYEDB8p8l255gi8nhxwtLfB/mdiUlpaGHTt2CF/iWrRogZkzZzLuflkSbGxshFEmq1evXmAS18+TvArbX1oUdfyfy+Xi9u3bAAATE5NS08ciPThU3CcrBJO6vL29oaOjI+JyVqBwDgd3794VSyDTpKamQltbW+hDnc+pU6cwefJkTJgwocCLi7QTKTCNrH6+qKgojBs3Dvv27YOZmRnTcsoskuwa9PHxwciRIxEZGQklJSWsX78e06dPL7OR4bhcrrBnoVu3bgV++3/bX1o0bdq00PH/Ro0aISAgAPfv30etWrVQo0YNZGVlFZjAyiJdUlNToaenV8CuSAKxE7rIQnKX0iQ9PR0zZszA0aNH4eTkVOgMbG9vb7i4uCA6Ohr16tXDihUr0L9/fwbUViw0NTXB5/N/Gz+epWhIwhDxeDysXbsWK1asAJ/PR+3atXH27Fk0adJEAgqZJf+FREFBodDv6m/7S4MVK1ZgwIABBcb/86/H+PHjMWjQIGzatAmampqMaARkt4EgbaT5OUtc8qdPn2BmZlZm37iLC5fLRUBAAJycnBAZGYnDhw9jxIgR4HA4Ii2bshagoiSIG9RCkmRmZmLVqlWYN28e9PX1hQ8IcXVVlIeLRHn1CnBwQMzBgxi+erXwhX/EiBHYvXs3o8ajotG/f394enrC1dUVUVFRqF+/PqZPn46OHTtCSUkJ9+/fl0qyKhbmKfGTq3bt2oiNjRUG9B8yZAh27NhRIBVqeWHevHk4efIkqlevjs2bN6Ny5cq4fv16gePKWoCKkiBOUAtJk5SUhOPHj0NLS0uirUA2qEUJyMnB1Y8fMap/f3xNToa6ujr27NmDkSNHMq2sQtKvXz/hM6dr165o2LAhgoKCsGfPHrEnorLILiU26r8OxV+9elUiaeNklSNHjqBf27aY0qcPlHNykBsXhyxDQ8jl5kLzp0xulaOiUIkIQT/+rwNAnQgKkZHQ/vgRAJBZpQryNDWhlJIC1R9uWvlwVVWRYWQE8HjQ/vy5gI5UExOQggLUYmOh+MsMyixdXeRWqgTF9HSo/TL7mKekhPQfb+ZaHz/i1/6VtOrVwVdWhmpCApTS0kT25ejoIFtPD/KZmdCIjUVwcDCuXLmC79+/o1KVKmg6ahRat24Nzc+fIcfjiZybXrUqeGpqUElKgvIvYYNzNTWRVaUK5HJyoPnLBB4CkPrDpU0jMhLyPzLcAT9mUoeFoUWnTqgqJwcPFxcoKSgAP77fPDU1ZFatCg6XC60vXwp8hymmpoC8PNRjYqDwkx88AGTp6yNXWxv49g349ftXVwfyw98WltfAwgJQUQHCw4Ff3YKqVhUsqalAWJjoPmVloEEDwd8vXhQMsVqnDqChAURFAb9kHYS+viB3eWYm8Pat6D45OeBHsiO8fg38CNMrpGZNQEcHiIsDYmJE9+noCPbn5gKhoQU/q5WVoPz374H0dOTm5WHRqlXYAgDJybCyssLZfftQR1FR9LvS0BB8Hj4f+OGCKULDhoCSEvDpE/BrmGkjI0Eo2uRkwf6fUVERfP+AoFw+X3R/vXqAmhoQESHI+/4zVaoA1asD6emCz/MzCgpAvjfJq1dARobwPkZgoKBcLS0gNlawcLn/3x8eDtSuLfjef+QXECHfl//dOyAjQ3SfqSmgqwskJgK/ZorU1BSUy+MBISEFy23UCFBUBD5+hMqP34SCgQGOLVqE+q1bC475/l2g72dUVYH69QV/BwUBv063ql9fcMyXL0BSkug+AwOgWjUgLQ34NRKgoqJAEwC8fAnk5f1/H5cL+cxM8NTUgOjoguXq6QEmJkBWFvDmjeg+DgfIf5F/80ZwzM+YmQGVKgHx8YKyf0ZbG6hVS6Dl5UsUwNISkJcXfJZfnoeoUQOoXFm8Z0Qhz3aJQSWEw+FQfHy88H8NDQ36+PFjSYuTWVJSUggAnRL8xIULd+hQysrKouzQUJHt+Qt+LE8K2Zfj7k5ZWVmUu21bgX1cW1vKysqirPj4QsvNioigrKws4vbsWWBf7vr1lJWVRTknTxbYx7OyEpSblUV8JaUC+7MDAigrK4vyRo8usC9v3jxBuTduFNgX+eNznj59mvhGRgU/640bgnLnzStY7ujRgu8wIKDAPr6SklAvz8pKZF8EQGrKyuTu7k6569cX/A579hScGxFR+HcYHy/4Dm1tC36H27YJ9h09WlBTy5aUl5dHeXl5hZab9+YN5eXlEc/RseD3v3Qp5eXlEffKlYLl1qolLJevr1+w3IcPBeU6Oxcsd/Jkwbl+fgXL1dT8f7kWFgW/p/PnBZrc3AqWO2CA4Nzw8MI/a3q6oNz27SkMIOuffvPTra0pLS2N6ODBgud26CC4sbKzCy2XIiMF+wcOLLhv7VrBvv/+K7jPwuL/N62mZsH9AQGCfVOnFtw3e7Zg35MnBffp6/+/3Fq1Cu6/fl2wb/nygt+ho6Ng34cPhX/WfFq1KrjvxAnBvl27Cu7r1i3/4VR4uQkJRESU17Mn2QA08+d9W7YIzvX0LHhekyb/11TIM4JCQwX7xo0ruG/BAsG+e/cK7qtW7f/lVqtWYP/jNWvo4sWLxHNxKXjuuHGC8wp7ziop/b/cJk0K7vf0FOzbsqXgvt69BfsSEgr/DlNSBPu7dSu4b9cuwb4TJwrua9Xq/5oKK/fDB9q3bx8tbdiQAFBKfj0SpESz3wFAXl4ecXFxqFy5MgDBJKUXL16Uu1nH+bPfr65bB/Wfus9z1dULbamHhITgyNGjCP7RBV8XgBqAsWPGCP3Hy3JLfc/s2YiNjUX+jyYPwCsOB6ampnCfPVuqLfWw6GiYGhpCQV4en+XlUcnUFErfv0P12zeRcyXRUldMS4PaL61irrIyMn5ECBS2xn7+DmvUAF9JCapxcVD6peWVXakScnR1oZCZCfXYWJF9PEVFpBsbAwA0w8Mh90srM93ICDxVVah8/QrlXyYB5mppIatyZcjn5EDj1++Qw0Hqj/C8GhERkP+5hQQgw8AAXA0NKH//DpVfv0N1dWQaGoKTlwetiIgCnzXFzAyQk4PfhQvYduoUMnNyoK2igmPZ2ai8ejWSLC3R28ZG0DL+mTLeUudmZODJkycABJHzFH5pqXO53P/v79kTCgy11ElBAbz377FqzRqYGBhg1KBBgnki1aoJWtUy0lLncrm4GR4OnpoaujduDIVy3FLn8XiQb9QIUV+/wnPvXsxdu1Yqs99LbNTl5OTQvXt3YdaxS5cuoXPnzgXCgspicpHikG/Uz5w5U+TsZ48fPy4QoMLGxkbKSkuHAQMGIO8X4wAAioqKIvnRJc33798xYcIEjBs3TiZDclY0srOzceDAAaGPs4WFBVynTkWdr1/xvV49cNXUyuW8hL/N1mZ6NjcRYcyYMahTpw5cXFxkZmb5774Xpr+v0uLmzZuYM2cOHj58CF1d3d+6SkuCEn+Do0aNEvlfkpm0ZJGuXbsW+cvv2rWrIP41mPVVlQalHf/627dvqFSpEjgcDqpWrYq2bduKJH1hKX1evnwJJycnvHnzRhgmevHixeXqd15W4XA4qFu3Lox/9PywyAb16tUrtWdXie/CI0eOSFKHzFNcn1NZ8FWVBn/yf5X050xKSkLjxo3h5uaG8ePHo2vXrhItn6V4EBH27duH2bNnIycnB0ZGRjh58uT/A1DFxgL79wOTJgkmBbKUCkSEnTt3QkdHByNHjsTChQsBMO9uWtEJCQnBggUL4OHhAWNjY+zbt69U6mXOF4mlTJLv/2pqagpFRUU0atQI58+fl6gPPv/HeKienh6WL1/+11z0LNLn+/fvGDRoEKZOnYqcnBz06NEDwcHBohElY2OBlSsFa5ZSg8Ph4MWLF3j7q/cDCyMQEfbs2YOWLVsiNjYWyb/ODZEy5acJyVJq/Oz/KulxsJSUFHTv3h0uLi7o27cvpkyZIrGyWUqGr68vHB0d8eXLFygqKmLDhg1wdnZmND4BUygoKMjMXIGHDx8iJycHXbt2xYEDB2T6elSUKJvfvn3D+PHj4e3tjenTp2PTpk2lHp+ENeosMoWWlhasra1haGjItJQKD5/Px4YNG7B06VLweDzUqlULZ86cgbW1tUTrKU/dxP/++y8WLlxY5Bzm4rB582YoKioKe0v4v874B0TmuTD1Pf8pymbv3r2F83PK+u/g8ePHGDlyJNLS0vDvv//CwcEBQOGfq0ykXmX5/0xOIgIRyfSbsyyRk5ODOXPmYNiwYWjTpg127NjBtKQKyc8Pmri4OIwePRp37twBAAwdOhS7d++GlpbW7x9IXC4U8sspxkPr6tWr4siWGXx9fbF+/foCOcwXLFggMe+XpKQkpKWlwdTUFMOGDYOysrLMf3+/i7Lp4uICRUVFYXTK69evF/rMlPVQ5DweD15eXvDw8EDdunWxfPlyKCkp/TF9rcylXmVhkSTy8vJ4//49vnz5gjZt2jAtp8KS/xAKDAzEtm3bkJKSAmVlZUyaNAldunTB48eP/3i+alwc6nXogLchIcj6JU7Cn4j81Q+7jHL8+PFCjdeJEydQ/Ud8A3HZu3cvsrOzMXv2bImUVxpERUXhV89pIkEa2KioKGT88NGPiooq1IDL+kx+Pz8/nDp1CoMGDYKjoyPk5eUZ1SO2UV+3bh0MDAwwduxYke3u7u5ITEyEq6uruFWwlEOICO7u7mjdujXq16+PmzdvyvwbeXmHy+Xi5MmTwtgSJiYmcHFxKXLijyxDQwTNnVvseiVl8JgmMTGxUOOVkJAg1mfMy8tDWloadHV1MWvWLCgrK5epXPTVqlVDREREATfY6tWro1q1aoj+ERimWrVqhbbUZTUuxbt371C3bl3Y29ujf//+xco9kZqaKjVdYhv1/fv3w8PDo8D2Bg0aYOjQoaxRZymUnJwcbNmyBfHx8ahfvz5r0BkmPDwc69evh7+/PwBg8uTJ2LhxY/HybGdnC+LTV68uiPBWwfhTDIeePXuWuNxhw4YhMjISDx8+LJP3SW5ursiYev5606ZN6N69+1/zz8uiS7CPjw/at28PHx8ftG7dGs2bNy/W+VL9TOLGmVVWVqZPnz4V2P7x40dSVlYWt/g/wuVyacmSJWRqakoqKipUs2ZNWrVqFfH5fOExfD6fli5dSoaGhqSiokJdunSh9+/fF7mO/NjvRYnRm5eXRxcvXqT//vuPLly4QBcvXhTECS9n5H/Oknw+f39/io6OJiKSStxjluLj6elJWlpaBIB0dHTIy8urZAXlx/DPj7NewfDy8iIAxOFwRNbnz58vUXnZ2dlERBQUFETPnz+XpNRSx9PTk8zMzEhRUZEaN24s/E7EeZYwwffv34lIYFf+/fdf4vF4JSqnOHaluIht1M3NzelEfvKBnzh+/DiZmZmJW/wfWbNmDenp6dHly5cpPDyczp07RxoaGvTPP/8Ij1m/fj1pa2vThQsXKCQkhPr06UNmZmaUlZVVpDpYo16Qkt6Iubm5ZGpqSjNmzJCiOpaikpGRQRMnTiQIQu2TjY0Nff78ueQFVnCjTvR741Vc5s2bR3Z2diU2GrLG754Znp6eZGpqSoqKitSoUaOSv1CWAmfOnCFtbW26efOm2GVJ06iL3QcwYcIEzJo1C3l5eejcuTMA4M6dO3BxccHcEoyvFYcnT57AwcFB2LVlamqK06dPC7sQiQjbt2/HkiVLhO4Fx48fh4GBAS5cuIChQ4dKVR+LgMTERCgpKUFbWxs3btxgcznLAK9evcKQIUPw6tUrcDgcLFy4ECtWrCj3IXil7TbVu3dvYT6M/O7kktTZpUsX1K9fHzwer1BXtbIGl8st4Lr2J1c3abkBloTMzEzMnj0b7u7uGDx4MJo2bSr270imXdrmz5+PpKQkTJ06Fbk/cl6rqKjA1dVVGK5QWrRu3RoHDhzA+/fvUadOHYSEhMDHxwdbt24FIBgnjIuLg62trfAcbW1ttGzZEr6+vlIz6hwOBz169JDJsaDShsfjoX379ujQoQP27duHOnXqMC2pQkNEOHToEJydnZGVlQVDQ0OcOHFC5B4pz/zJzUgS8Pl8oYvWjRs3iuXWevPmTYSFhWHq1KkAgMqVK+P69etS0VnaFPa9/M7VzdXVtdQDtvyOz58/Y9OmTYiPj8f06dPRtWtXYRY+cZBplzYOhyMMUPHmzRuoqqqidu3awrdVabJgwQKkpqaiXr16kJeXB4/Hw5o1a+Dk5ARA4GsLAAYGBiLnGRgYCPf9Sk5ODnJycoT/S3OWYnmGy+WCz+dDSUkJu3fvRv38lI4sjJGSkoKJEyfC09MTAGBnZ4djx44VuD9YmEFRUVH4HGPaLao0iI6O/q2rG9MQEW7cuIFDhw6hatWq2Lp1q1Rc6361L8rKymLbTok1JTU0NIo9A1BcPD09cerUKXh4eKBBgwYIDg7GrFmzYGRkVCCLXFFZt24dVq5cWaJzK0ooxL/B5/Nhb2+Pxo0bY+vWrcJhGRbm8PPzg6OjI8LDw6GgoIC1a9di7ty5kg2Q1LRpwRzcMoa03aO4XO5fZ3P/zOPHj+Hv74/Zs2fLrOuWtCjtjI9FJTk5GZMnT4aXlxcmTpyIzZs3F88LpAjkG/Nf3UWXL1+OFStWiFV2iYz6nDlzsHr1aqirq2POnDl/PDa/K1wazJ8/HwsWLBB2ozdq1AhfvnzBunXrMGrUKGGo0fj4eFT9KWtUfHw8rKysCi1z4cKFIp8pNTUVNWrUAJfL/eM4SFkZH5IEhY2P5ZPfhebo6Ahzc3Oxx47YIQzx4PP52LJlCxYtWgQulwtTU1OcOXMGLVu2ZFoaI5TG76k4GRqfPn2KK1euYNasWYzPZ+jYsSOsrKywfft2AII5SrNmzcKsWbOkUl9pZnwsDikpKfD398e5c+cwcOBAqdSR//kiIyNFYg5Iooe7RN9cUFAQ8vLyhH//Dmn7VGZmZhZoacjLywsnlpiZmcHQ0BB37twRGvHU1FT4+fn9NlHI77o/bt26BTU1td9qKQvjQ5KisPGx/LFaPT099O/fH1WqVEFqaqrYY5iykjyjLJKQkICRI0fixo0bAIBBgwbhwIED0NHRkU6F794Bo0cDR48CdetKpw4JU9qGDABiY2Px9OlT9OvXD/Pnz8fcuXNL1YiNHj0ax44dK7Ddz8/vj8NkcXFxmD9/Pm7duoW0tDTUrVsXixcvxoABA0qkIz/jo6urK6KiolC/fn2sWLGCkUYQn8/Hvn37MGLECNSqVQthYWFQUlKSer1aWloSDyRUol/SvXv3hH8fO3YM1atXL2BciUjq4R979+6NNWvWwNjYGA0aNEBQUBC2bt0qjG7H4XAwa9YsuLm5oXbt2jAzM8PSpUthZGSEvn37SlSLLI8PSRo5ObkC40scDgdqamoS76ZiKRm3b9/GiBEjEBcXBxUVFezYsQPjx4+X7ot2Rgbw9KlgLWOU1JAdOHAAHh4eCAwMRFpaGr5//y72S9GuXbvg4eGBHj16QFlZmZEcEfb29jhy5IjItsqVK/9xLH/kyJFITk7GxYsXoa+vDw8PDwwePBjPnz8vVjS1n5FmxsfiEBUVhYULF6JKlSoYOHBgqRh0aSH2N2hmZobY2FhUqVJFZPu3b99gZmYGHo8nbhW/ZefOnVi6dCmmTp2KhIQEGBkZYdKkSVi2bJnwGBcXF2RkZGDixIlITk5G27Ztcf369WK3nrt27frHNypZHR+SNsePH4eSkhKGDh1arj9nWYHL5WL58uVYt24diAgNGjTA2bNn0aBBA6alMU5JDFlmZibs7e1hb28vljdPbm4uXr9+DSsrKyxbtkwY7pUplJWVC2RC/LXX4leePHmCvXv3okWLFgCAJUuWYNu2bQgICCixUWea+/fvw9raGsbGxggLC0PlypWZliQ2Yhv1X1un+aSnp0u921lTUxPbt2//7Y8QEBjWVatWYdWqVWLV9bfxMVkdH5I2Dx48gJaWFoYPH860lArPly9fMGzYMKHLzcSJE7Ft27Y/DhtVJEpiyPK74e/fvy9W3atXr8a+ffvw+fNnqKurl0nj0bp1a5w9exY9e/aEjo4OPD09kZ2djY4dOzItrdjk5eVh2bJl2LBhAzZt2oS5c+eWyWtSGCW2NvmTyTgcDpYtWyby4ODxePDz8/vtZLTyiCyND0mbwMBApKamomPHjjh8+HC5fmkpK5w/fx7jxo1DcnIytLS0cOjQIQwaNIhpWRWepKQk6OnpYd68eXBwcIC6ujrTkgAAly9fhoaGhvD/ovSyeXp6YsiQIdDT04OCggLU1NTg7e0Nc3NzaUqVOJ8/f4ajoyOePXuGdevWlamMd0WhxE/j/AlyRISXL1+KjEEoKSnB0tIS8+bNE19hGUJWxoekzYoVK8DlctGxY8dy+xnLCtnZ2Zg7dy727NkDAGjRogXOnDkDMzOz0hdjagqcOCFYyyAlMWTisGPHDmzduhWvX7+GtrY2rK2tpVpfcejUqRP27t0r/F9dXR2Ojo5/PGfp0qVITk7G7du3oa+vjwsXLmDw4MF49OgRGjVqJG3JEsHLywvjxo2Djo4OHj16JLE897JEiZ/I+ZPlxowZg3/++adMpQJkKT5fv35FYmIi6tevj2PHjrFdujLAmzdvMHToULx48QKAYP6Im5sbc65RurqADA/DlMSQlYT84DEDBgyArq4uNDU1JV6HuKirqxerhf3x40fs2rULoaGhwvkZlpaWePToEXbv3o19+/ZJS6pEyMrKwpw5c7Bv3z4MHDgQBw8elJ4XCMOIPe3yyJEjrEGXMB07dhRxqTE1Nf3jvIHSYOzYsRg3bhyICJUqVWJ0kk9Fh4hw5MgRWFtb48WLF6hSpQquX7+ODRs2MOvrnJgI7N4tWMsg+YYsf/k5doWk8PLywrx585CdnY1q1aph9OjRZTJd6q/khzX9kwuxrBIfH48WLVrg6NGj2LdvHzw9PcutQQckYNQB4NGjRxg+fDhsbGyECe9PnDgBHx8fSRRfbsm/4X9dNm7ciNWrV//xXF9fX3Tu3Bnq6urQ0tJC+/btkZWVJVF9PB4PX79+BQBs374d586dKxcPqLJMamoqhg8fjrFjxyIzMxO2trYICQmBnZ0d09KAyEhg+nTBuoLSqFEj9OjRA7169SpXQ1P16tWDubk5Jk2aBH9/f3z8+BFbtmzBrVu3JO4eLGkqV66M9u3bw9/fH5MmTSr3zzCxjbqXlxfs7OygqqqKoKAgYdz0lJQUrF27VmyB5R17e3vExsaKLM2aNftjl52vry/s7e3RrVs3+Pv749mzZ5g+fbrE/V3HjRuHPn36gIhQs2ZNVKtWTaLlsxSP58+fo2nTpvDw8IC8vDzWrl2LGzduFJjRzSI54uLiEBwcjLCwMADAy5cvERwcjG/fvgmPefbsGYYPHw4ulwsLCwvs2LGj3MVrUFRUxNWrV1G5cmX07t0bjRs3xvHjx3Hs2DH06NGDaXkFyMzMxPDhw3Hv3j3Iyclh9+7dZWbcX1zEfpV0c3PDvn37MHLkSJw5c0a4vU2bNnBzcxO3+HJPSdxsZs+ejZkzZ2LBggXCbXUlGMGLy+VCQUEB06dPR0ZGRrl/s5V18lMIu7q6Ii8vD8bGxjh9+jRat27NtLRyz759+0RyQbRv3x6AYNhx9OjRAAQ+6F++fEFycjL09fWZkFksjh49Wuj2X932Pn/+LPJ/7dq14eXlJR1REkZZWRmZmZlISUkp9bq5XK4wkiYTE6bFru3du3fCH/rPaGtrIzk5WdziWX4hISEBfn5+cHJyQuvWrfHx40fUq1cPa9asQdu2bcUuf+XKlXj+/DkuXrwoU7N1KyqJiYkYM2YMrly5AkDgOnno0CFUqlSJYWVli5IashUrVhSaYCM+Ph5r167FwoUL0aZNGzx8+JB9+WUYPp+P7du3o1mzZujQoQPOnz/PtCRGELu/1tDQUNg19TM+Pj6oWbOmuMWXe/LdbPKXv/kWf/r0CYDgYTNhwgRcv34dTZs2RZcuXfDhwwex9TRv3hxdu3YVuxwW8bl//z6srKxw5coVKCsrY8+ePfj3339l16BragLdugnW5ZynT59ix44dwjDQFcGgy+IE3nwSExPRq1cvzJ07F0+fPmVaDqOI3VKfMGECnJ2d4e7uDg6Hg5iYGPj6+mLevHlYunSpJDSWGRQUFIqdgKS4bjb5M00nTZqEMWPGAACaNGmCO3fuwN3dHevWrSu27jNnzsDPzw/btm2TyfGxigaXy8Xq1auxevVqEBHq1auHs2fPonHjxkxL+zO1awM/kseUR/Ly8nD16lU4ODjAwcEBXbp0EfF7Ly+UJE7+t2/fsHz5cty8eRMRERGoXLky+vbti9WrV0NbW1uqeu/duwcnJydht7e9vb1U65N1xDbqCxYsAJ/PR5cuXZCZmYn27dtDWVkZ8+bNw4wZMyShsVxTXH/RfDccCwsLke3169dHREREiTRkZmbi69evwrF0FuaIjIyEk5MTHj16BEDgSrhjxw6ZiUT2R3g8QTIXdXXgD/HUyyre3t5wcnLCmzdvYG5uXi4Nej7FjZMfExODmJgYbN68GRYWFvjy5QsmT56MmJgY/Pvvv1LRyOVysWrVKri5uaFTp044efKkVNwUyxpiP8E5HA4WL16M+fPnIywsDOnp6bCwsCjXP3gmMTU1hZGREd69eyey/f3798WKkBUcHIwnT55g6tSpGDNmjDCzHQtzXLx4EWPGjMG3b9+gqamJ/fv3SyU4itQICQGaNQMCAoCmTZlWIzHev3+POnXqYNCgQWjcuHGZC4taEoo7gbdhw4Yik+hq1aqFNWvWCL0CJN1YiIiIgJOTE3x9feHm5gZXV9c/JuapSEjkm75z5w7u3LmDhISEAoEI3N3dJVEFyw84HA7mz5+P5cuXw9LSElZWVjh27Bjevn1brDfiq1evwsvLC+PHjy/TaQbLAzk5OXBxccGOHTsAANbW1jhz5gxq1arFsDKWS5cuoV+/fnjx4gUsLCxQr149piWVGVJSUqClpSVxg56dnY3WrVtDTk4ODx48QJs2bSRafllH7G975cqVWLVqFaytrVG1atUKMWGEaWbNmoXs7GzMnj0b3759g6WlJW7duvVXI/Dt2zf4+/vD3t4erq6umDNnDmvQGeb9+/cYOnSoMJfC3LlzsXbtWva6MExGRgbU1dXRvXt3nD179o8518sj4sbJ//r1K1avXo2JEyf+8bjizEPKzs4GAKioqODIkSNo1qwZdHV1i6WrQkBiYmhoSMePHxe3GJklJSWFAFBKSgrTUsRm8eLFZGRkRJmZmUxLYSGi48ePk7q6OgEgfX19unLlCtOSxCMggAgQrMswDx8+JH19fXrz5g3TUhhh1KhRZGtrSx8+fBAuMTEx1KFDB3J2dhYeZ2JiQtu2bStwfkpKCrVo0YLs7e0pNzdXIpp4PB7Z2NjQ9OnTJVKeNMnLy6OLFy/SxYsXKS8vr9BjpGlXxG6p5+bmskEwZBgej4f379+jfv36WLJkCSZNmlTuol2VNdLT0zFt2jQcP34cgGCs8uTJk2zEPoYhInA4HDRr1gxTpkyBsbEx05IYo7gTePNJS0uDvb09NDU14e3tLXYuAiICn8+HvLw8Zs2aJdEgW+UVsf3Ux48fDw8PD0loYZECa9euRbt27ZCeng4VFRXUqFGDaUkVmqCgIDRt2hTHjx+HnJwcVq9ejdu3b5cPg96oEZCQIFiXMV69egUbGxvExMRATU0Nq1atYjMRFpPU1FR069YNSkpKuHjxojANdUlJS0vDyJEj4ezsDAAYPHgwLC0tJSG1XCN2Sz07OxsHDhzA7du30bhx4wJvZlu3bhW3CpYSkJaWBk1NTUyfPh3t27dnvREYhoiwc+dOzJ8/H7m5uahevTo8PDzQrl07pqVJDkVFoHJlplWUCAMDA1StWhV5eXlMSymT5Bv0zMxMnDx5EqmpqUhNTQXwZ1e43xEYGIghQ4YgLi5O5tO6yhpit9RfvHgBKysryMnJITQ0FEFBQcIlODhYAhJZisv+/fvRsGFDJCcno1KlSujQoQPTkio0SUlJ6Nu3L5ydnZGbmwsHBwcEBweXL4MOAB8/An36CNZlgMTEREycOBEpKSnQ19eHt7c3TExMmJZVJgkMDISfnx9evnwpTGubv0QWI2sfEeGff/5Bq1atoKWlhcDAQDg5OUlReflD7Jb6vXv3JKGDRQLkjwn26NEDOTk5bJ57GeDRo0cYNmwYoqKioKSkhC1btmDatGnl00skJQW4dAkoJFa6LJKWloZ79+7hw4cPbJ6DnyhJnPyOHTuCiMSq9+vXrxgzZgwuX76M2bNnY926dVBWVharzIqIZHN1sjDGlStX0K5dO2RlZaFGjRqYOXOmxFOxshQdHo+H1atXo2PHjoiKikKdOnXg5+eH6dOnl0+DXkbgcrnYtWsXcnJyULNmTbx9+5Y16DLAgwcPYGVlBV9fX1y6dAlbt25lDXoJEbulPmfOnEK3czgcqKiowNzcHA4ODqw/oZQxNjaGubk5uFwu01IqPNHR0Rg+fLiwZTNq1Cjs2rWLndcgA7x79w6urq6oW7cuunbtykYhkwGICMuWLYO5uTlOnTpVPiaNMgiHxOwz6dSpEwIDA8Hj8YTuBu/fv4e8vDzq1auHd+/egcPhwMfHp0C88rJAamoqtLW1hdGRZInQ0FDs3LkTe/bsYR9OMsKVK1cwevRofP36Ferq6ti7dy9GjBjBtKzSITBQZsPEPnz4EG3btoWcnBwSEhJQpUoVpiVVeKKiopCUlARLS0skJSVBR0enXDzHipJPXZp2Rez+WQcHB9ja2iImJgYBAQEICAhAVFQUunbtCkdHR0RHR6N9+/aYPXu2JPQWIL9VpKenB1VVVTRq1AjPnz8X7s9/C6xatSpUVVVha2srkRSlskBSUhL8/f2RmJjItJQKT25uLubOnYtevXrh69evaNKkCQIDAyuOQQeAatWALVsEaxni7du36NixI7y9vQGANegywuTJkzF9+nQAgJ6eXrkw6DKBuNFrjIyM6NWrVwW2h4aGkpGRERERBQQEkJ6enrhVFeDbt29kYmJCo0ePJj8/P/r06RPduHGDwsLChMesX7+etLW16cKFCxQSEkJ9+vQhMzMzysrKKlIdshZR7tu3b7R9+3bi8/lEJIi0xMIsHz58oGbNmhEAAkDOzs6UnZ3NtKwKT0xMjPDvJ0+eCO8ZFubIzs6mjx8/EhFRZGQkff36lWFFkofpiHJiG3V1dXW6d+9ege337t0jDQ0NIiL6+PEjaWpqiltVAVxdXalt27a/3c/n88nQ0JA2bdok3JacnEzKysp0+vTpItUha0bd29ubdHR0KDw8nGkpLER06tQp0tDQIACkq6tL//33H9OSmOPbNyJPT8GaYT59+kRqamp07tw5pqWw/OD9+/fUtGlTql+/PnG5XKblSA2mjbpEut/Hjh0Lb29vREVFISoqCt7e3hg3bhz69u0LAPD390edOnXEraoAFy9ehLW1NQYNGoQqVaqgSZMmOHjwoHB/eHg44uLiYGtrK9ymra2Nli1bwtfXt9Ayc3JyhIETfg6gwCR8Ph937twBAPTt2xcfP36Eqakps6IqOBkZGRg7diycnJyQnp6Odu3aISQkBH369GFaGnOEhwODBwvWDJEfPMbMzAw7d+4sdiISFulw6tQpNG3aFGlpaTh58iTb1f6DX21NTk6O+IWK+1aQlpZG48ePJyUlJZKTkyM5OTlSUlKiCRMmUHp6OhERBQUFUVBQkLhVFUBZWZmUlZVp4cKFFBgYSPv37ycVFRU6evQoERE9fvyYAIh0wxERDRo0iAYPHlxomcuXLxd2o/68MNlS//fff0lOTo7evn3LmAaW/xMSEkL16tUjACQnJ0fLly//7Rt5hYLhhC4xMTFUp04dunHjBiP1sxQkLS2NRo8eTQBo+PDhlJqayrQkqVOclvqvy/Lly8WuX2yXNg0NDRw8eBDbtm3Dp0+fAAA1a9YUcd+xsrISt5pC4fP5sLa2xtq1awEATZo0QWhoKPbt24dRo0aVqMyFCxeKuOmlpqYyFi89MjISNWrUQP/+/eHv788mM2AYIsLevXsxZ84c5OTkwMjICKdOnULHjh2ZlsYCQajXXr16oWbNmkxLYQEQHByMoUOHIioqCseOHcPIkSOZliRzREZGisx+l4RvvsSik2hoaKBx48Zo3LhxqfnjVq1atYCbXP369REREQEAMDQ0BADEx8eLHBMfHy/c9yvKysrQ0tISWZjgxo0bMDc3x8uXL4WZo1iY4/v37xgwYACmTZuGnJwc9OzZEyEhIaxBZ5jk5GQMGDAAgYGBkJOTw5YtW0qUXYxFsuzfvx+tWrWCiooKAgICWIP+G361NZIw6mK31PN5/fo1IiIikJubK7JdmmOMbdq0wbt370S2vX//Xhi/2czMDIaGhrhz546wtyA1NRV+fn6YMmWK1HSJQ25uLpSUlNC5c2fs378fDRo0YFpShefJkydwdHREREQEFBUVsXHjRjg7O7OR4X5FVRVo0kSwLrUqVZGRkYFv376VWp0sfycrKwsTJkzApk2bxM7WxlJMxO2///jxIzVu3Jg4HA7JyckRh8MR/i0nJyf2+MCf8Pf3JwUFBVqzZg19+PCBTp06RWpqanTy5EnhMevXrycdHR3677//6MWLF+Tg4CCzLm1Pnz6l6tWr07t376ReF8vf4XK5tGbNGpKXlycAVKtWLXr+/DnTsio8XC6X3NzchK5RLLKBj48PbdiwgWkZjFPmZ787OzvDzMwMCQkJUFNTw6tXr/Dw4UNYW1sXSAAgaZo3bw5vb2+cPn0aDRs2xOrVq7F9+3aRrD4uLi6YMWMGJk6ciObNmyM9PR3Xr1+XybfHBg0aCGfyszBLbGws7OzssHjxYvB4PAwbNgyBgYHsMIgMkJGRgWPHjuHBgwdMS2H5iadPn+Ly5cts+lqmEfetQE9Pj0JCQoiISEtLSzhD+86dO2RlZSVu8Ywj7Zb6hw8fqEuXLhQbGyuV8lmKz7Vr16hy5coEgNTU1OjIkSNs4JKiEBhIpKQkWEuBK1euUEJCAhFRkXvaWKRLdHQ0ubu7E5EgEBbrBULk6elJpqampKioSI0aNSIvL68Cx0jTrog9ps7j8aCpqQkA0NfXR0xMDOrWrQsTE5MC490sBcmfiJeamvrbyXsspUNubi6WLFmCTZs2AQAaN26Ms2fPol69egwrKyMQAbm5grWESU9Px+jRozFr1iwsWrRIaj1tbEKkonP9+nWMHTsWSkpKcHBwED7LKvJ36O3tjcGDB4PD4YCIEBoaigEDBsDT0xP9+vUTHifN70hso96wYUOEhITAzMwMLVu2xMaNG6GkpIQDBw6wriW/ISUlBStXrsTKlStRpUoV3L59m2lJFZ5Pnz7B0dER/v7+AIBp06Zh8+bNMjlMU5EICwtD1apVoaGhAT8/P6kHXcpPxMHye/Ly8nDy5El4e3vD2toazs7OePz4MdOyZAIXFxehQQcEbrAcDgeurq4iz5LMzEypaRB7TH3JkiXg8/kAgFWrViE8PBzt2rXD1atXsWPHDrEFlkcSExNx7tw5vHz5kmkpLAA8PT3RpEkT+Pv7Q0dHB+fPn8euXbtYg84wWVlZaNu2rTAOhZmZGetxwDBxcXFYsGABLl26hHHjxmHJkiXQ1tZmWpbMEB0dLTTo+RARoqKiSk2D2KlXC+Pbt2+oVKlSubgBJZUij8/n48SJE3B0dISSkpLQdY2FOTIzMzFr1ixhaOHWrVvDw8ND6BLJUkwklHo1PT0dKioqUFBQwP3799GsWTPhEJ+0qchdx3/D09MTU6ZMQeXKlXHy5ElYW1szLUnmaNq0KUJDQ0UMO4fDQaNGjRAQECDclpqaCj09Pemk9Jb4KH05Q1ITGl6/fk1KSkp0+fJlCSljEYeXL1+ShYUFASAOh0OLFy9mJ/mIS2YmUWioYF1CcnJyqEGDBhIJl8kiOdatW0cAyNHRUWaSW8kiXl5ewmfKz+vz58+LHCfNiXIlbqmPHTu2SMe5u7uXpHiZQdyWemBgIKysrCAnJycM+8rCHESEgwcPwtnZGdnZ2TA0NMTJkyfRpUsXpqVVaPIfQxwOB0ePHoWNjQ0bFlkG4PF4kJeXx4cPH+Dj44PRo0eXix5YaXLu3Dm4uroiKioK9evXx4oVK0QmyQGS6wEujBIbdTk5OZiYmKBJkyYFxhB+xtvbu8TiZAFxvvywsDDUrVsXHh4eGDJkiJQUshSV5ORkTJw4EefOnQMA2Nvb49ixY2xcAEnx5QuwejWwdClQjCEMLpeLQYMGoWvXrpg6daoUBbIUBy8vL7i5ucHHxwfq6upMyykzcLlc4YTL7t27Q0Gh4Hx0aRr1Ek+UmzJlClJSUhAeHo5OnTrh8OHD8Pb2LrBURPJDVpqbm+PmzZsYOHAgw4pY/Pz80KRJE5w7dw4KCgrYtGkTrly5whp0SZKUBBw+LFgXAwUFBTRo0ADVq1eXkjCWktCwYUO0b98ecnISSxHCCHFxcXB2doa5uTlUVFRgYGCANm3aYO/evQVmoRMRunfvDg6HgwsXLojsu3PnDlq3bg1NTU0YGhrC1dVVNudgiNN3n52dTR4eHmRra0tqamo0aNAgun79erkK1FHcsY8vX75QpUqV6Ny5c1JWxlIUeDwebdiwgRQUFAgAmZmZkZ+fH9OyyifFSL3K4/HIzc2twFgjC7M8efKEunfvLkybXdb5+PEjGRoaUr169ejs2bP0+vVr+vjxI124cIF69OhB//33n8jxW7dupe7duxMA8vb2Fm4PDg4mJSUlWrlyJX348IHu379P9erVo7lz5xaok+kwsRKbKPf582dasWIF1axZk4yNjSktLU1SRTNKUb98Ho9HRER8Pp927NhB3759Kw15LH8gLi6OunXrJsxVPGTIEEpOTmZaVvmlGEadz+fToEGD2FjhMgKPx6N169aRvLw82djYUExMDNOSJIKdnR1Vr179ty8pPzdAg4KCqFq1ahQbG1vAqC9cuJCsra1Fzr148SKpqKgUyBHPtFGXWL+KnJyc0Omex+NJqtgyQUJCAqytrXH37l1wOBzMmDEDlSpVYlpWheb27duwtLTEzZs3oaqqioMHD+L06dOsTy3DXLt2DY8fPwaHw8GZM2fg4uLCtKQKT1xcHOzs7LBo0SK4uLjgwYMHqFq1KtOyxCYpKQk3b97EtGnTfjsnIH/SX2ZmJoYNG4bdu3cXGtkzJyenQNwKVVVVZGdni7iqyQJiGfWcnBycPn0aXbt2RZ06dfDy5Uvs2rULERERpZZTXRbQ09NDixYtoK+vz7SUCk9eXh4WLVqEbt26IT4+Hg0aNMCzZ88wfvx4dtautDEwABYsEKwLgYiwfv16HDt2DADK/FhteeDmzZuwtLTEy5cvcfPmTaxduxaKiopMy5IIYWFhIKICXhT6+vrQ0NCAhoYGXF1dAQCzZ89G69at4eDgUGhZdnZ2ePLkCU6fPg0ej4fo6GisWrUKgCD5k0xR0ib+lClTqFKlStS4cWPavn07JSYmSqz7QJb4XTdJWloajRkzhoKDgxlSxvIrnz9/JhsbG2F3++TJkylTDJ9pFskQFhYmTPSUnJwsHKpiYY7c3FxydXUlAGRnZ0dxcXFMS5I4T58+LdRH/OPHj/Thwwdq0aIFOTs703///Ufm5uYiQ8b4pfudiGjLli2kpaVF8vLypKamJvTdP3PmjMhxTHe/lzj2+759+2BsbIyaNWviwYMHv02DeP78+ZJWIdMoKCggLCwMERERsLS0ZFpOhcfLywvjx49HcnIytLW1/9femcfllP7//3W33S1atJBSKdlClhIJ2cYyIaYZWyHbjKaMLJExxjbZZqwxDN9kFz6fzNhHUmFQo4REsiRUCq203+/fH326f25Fd913nVNdz8fjPO66zrnf53Wu677O+5xzXef9xu7du/HNN99wLatxkZtbFk3Oxgb4XwQ4IsKkSZPQrFkz/Pnnn2z4gyfs27cPGzZswPr16zF//vwG+dTE0tISAoGgQmKx8pwkampqAIBLly7h8ePH0NHRkdjOxcUFffv2FacQnzdvHubOnYvU1FQ0bdoUSUlJWLx4Me9ynNTYqU+ePLnRPc4kImzbtg2jRo2CmZkZIiIiGl0d8I38/HzMnz8fO3bsAAD07NkTR44cgbm5OcfKGiGJicCAAUB0NN63b4+8vDw0a9YMBw4cgIGBAdfqGAAePHiA9u3bY+rUqbCzs4O1tTXXkmoNPT09fPHFF9i2bRtmz579yXF1X19fzJgxQ6Ksc+fO2LRpE0aOHClRLhAIYGRkBAA4cuQITExM0F2GkMi1QY2d+t69e+Uoo36Qm5uLjRs3QklJCR4eHsyhc8z9+/cxbtw4cWIcX19frFy5ssGMCdZnRo8eDWVlZZw5cwatW7fmWg4DwOnTp+Hs7Izbt2+jU6dODdqhl/P777/DwcEBtra2WL58OaytraGgoIB///0XDx48gI2NDQwNDSudHGdqaipxc/Drr79i2LBhUFBQQHBwMNauXYtjx45BUVGxLg+pSmROvdpYePv2LVq1aoU7d+7UWXIJRuUQEQIDAzF79my8f/9efDc4ZMgQrqU1aogIRQCEAFasWFHhcSaDGzIzM9G0aVMMHz4c//nPf9CxY0euJdUZrVu3xq1bt7B69WosXrwYL168gFAohJWVFRYsWFCtCIbnzp2Dn58fCgsL0aVLF/z1118YPnx4LaqvGbWSpa0hUR7Ob9GiRVi7di3Xcho9OTk5mDVrFo4cOQIA+OKLL7B///5Kr7QZdcuM0aOR+9dfOCpjljaGfCAiBAQEYN68ebhw4QJ69erFtaRGQVVhYvPz83Hnzh306tWLX2FiGxvlrz4wuOPmzZvo1q0bjhw5AkVFRaxZswbnz59nDp0nOPXvj3G6ugAb/uCc7OxsTJgwATNnzsT48eMbxaP2+oKamhqePn1aa/aZU5cSvo2bNCZEIhE2btyI3r1748mTJzAzM8OVK1fg6+vbIGft1ic2bNiABQsWAADGeHvjqzdvgM6dOVbVuImKikK3bt1w7tw5BAUFYdeuXVBXV+daVqOmqKgIbm5u4mRSX375Za3ti50RGbwmIyMDI0eOxPz581FcXAwXFxfExsbC3t6ea2kMAEKhEEKh8LOZGhl1g0gkwm+//QYHBwcYGBjg1q1bLDskT1BWVq4Qka62kMuYemhoKEJDQ5Geng6RSCSxrrHnU2fUnLCwMLi6uiI1NRWqqqrYvHkzvv32W/bWAcdcuHABjx8/hoeHh+SKu3eB4cOBc+fY3Xodk56ejilTpuD8+fPw8fHBL7/8AhUVFa5lNUrKx9SJCImJibC1tYWjo6PENrxMvVrOihUrMGTIEISGhuL169fIzMyUWOqStWvXQiAQwNvbW1xWUFAAT09P6OnpoUmTJnBxccGrV6/qVBejepSUlODnn3/GoEGDkJqaig4dOiAqKgrfffcdc+g8ICwsDGfPnq1wAY/iYuDly7JPRp3y6tUrxMfH49y5c1i/fj1z6DxAJBLh1KlT+Oeff+p2x7KGpDM0NKT9+/fLakZmoqKiqFWrVmRtbU1z5swRl8+aNYtMTEwoNDSUbt68Sb169aLevXtLbbc2w/kxKpKcnEx9+vQRh3qdMWNGg0kDWZ958uQJnTlzhojKwmBWGuq1GlnaGLJTXFxMW7Zsofz8fCIqC/3K4J7Q0FDy9/enkydPfjJMNa+ztBUVFaF3796ympGJvLw8uLq6Yvfu3RLZ0bKzsxEQEICNGzdi4MCBsLGxQWBgIK5du4YbN25wqJhRGX/99Re6dOmCq1evQlNTE0eOHMHu3bs/GQmKUXf8+uuv8PX1RWlpKZSUlNgERR6QmJiIH3/8EZcvXwYAFnSJB4hEIsybNw+nT58GwE2byNwzZ8yYgcOHD8tDS43x9PSEk5MTBg8eLFEeHR2N4uJiifL27dvD1NQU169fr9RWYWEhcnJyJBZG7VJQUIDZs2dj9OjRyMzMRI8ePXDr1i2MHz+ea2mNmvL3aQFg/fr1uHLlCnsLhAdcuHABhYWF6NChA54+fcqCLvGAFy9e4OXLl1BQUMDp06cxa9Ysqb73sa8pLCyUWYvMEeUKCgqwa9cuXLx4EdbW1hWuTDZu3CjrLj5LUFAQYmJi8O+//1ZYl5aWBhUVlQqRrZo3b460tLRK7a1ZswYrVqyoDamMSkhISMD48eMRGxsLAFiwYAH8/PzYmCAP8PHxwenTp5GYmChdKuU2bYCwsLJPhtwpKCjA/Pnz8fvvvyMgIADTpk1jMfV5ABFh5MiRaNeuHYKCgmBoaCj1xa+JiYnE/8uWLcPy5ctl0iOzU79z5w66du0KAIiLi5NYV9uTmp4/f445c+YgJCREbq8LLF68GPPmzRP/n5OTU6HiGfJh//79+P777/Hu3Tvo6+tj//79vAy72JggIrx58wb6+vr4+eefMWvWLOkfIWpqAv3716q+xsqDBw8wbtw4JCQk4Pfff8fUqVO5ltToKSwsRGFhIbS0tLB37160bNmy2jaeP38uMftdKBTKrEtmpx4WFiaziJoSHR2N9PR0iSw5paWluHz5MrZt24a///4bRUVFyMrKkrhbf/Xq1SejkJW/d8uoPXJzc+Hp6YkDBw4AAAYOHIgDBw6Isx8xuGPFihU4dOgQ7t69i2bNmqFZs2bSf/nlS2DbNsDLCzA2rj2RjQgiwr59++Dp6QlTU1NERUWx6HA8gIjg5OQEAwMDHDlypMbpt7W0tOT+SpvcErrEx8cjOTkZRUVF4jKBQFAhdZ08GTRokDhDVzlTp05F+/btsWjRIpiYmEBZWRmhoaFwcXEBUPa4Nzk5mQUv4YiYmBiMHz8eiYmJUFBQwMqVK+Hr68vGajmmtLQUioqKcHNzg5WVVc2efL16BaxdC3zzDXPqciA3NxceHh44dOgQpk2bhq1bt7JJozygvK8sWLAAzZs351pOBWR26k+ePMGYMWNw9+5dCAQCcWSp8kfvpaWlsu7ik2hqaqJTp04SZRoaGtDT0xOXT58+HfPmzYOuri60tLQwe/Zs2Nvbs+QGdQwRYevWrVi4cCGKiopgYmKCw4cPo0+fPlxLa/Ts2bMHe/bswaVLl2BpaQlLS0uuJTV6nj17hsGDB+PVq1c4dOgQJk6cyLUkBiAejvL398ewYcO4llMpMs9+nzNnDszNzZGeng51dXXcu3cPly9fhq2tLcLDw+UgUTY2bdqEESNGwMXFBf369YOhoSGCg4O5ltWoePPmDZydneHt7Y2ioiKMHj0asbGxzKHzhM6dO6NPnz4ssA+PMDIywsCBAxETE8McOo+wtbVFjx49uJbxeWR90V1PT49u375NRERaWlr04MEDIip7Ab9r166ymuccFnxGNiIiIsjY2JgAkIqKCm3bto1EIhHXsho9YWFhNHXq1MqDyNQUFnxGJrKysmjMmDEUFRXFtRTGB2zbto3Wr18vV5u8Dj5TWloKTU1NAIC+vj5SUlIAAGZmZkhISJDVPKOeUlpaihUrVmDAgAF4+fIl2rVrh8jISHh6erI7Qh6Qn5+P58+fIy8vT35G9fSA6dPLPhnVRk1NDYWFhcjOzuZaCuMD0tLSkJqayrUMqZE5oUvfvn0xf/58jB49GhMnTkRmZiZ++ukn7Nq1C9HR0RVec6tvsIQu1efly5dwdXVFREQEAMDd3R3+/v7SvevMqDWSk5Nx7NgxcapUImIXWBxTWloKPz8/ODk5wcbGhms5jP9x9epVpKSkYOzYsbXST3id0OWnn34SJ3ZYuXIlnj59ir59++Ls2bPYunWrzAIZ9YvTp0+jS5cuiIiIQJMmTXDgwAEEBgYyh84DwsLC4O/vjzdv3gCohTgS+fnAvXtln4wqefHiBQYOHIgVK1ZUGjyLwR379+/Hnj176uWFr1xSr37M27dv0bRp03pXGZXB7tSlo7CwEL6+vti8eTMAoHv37ggKCkIbFl2MUwoKCnDx4kWMGDECRIR3797V3gVWTAxgYwNERwMfxI5gVOTUqVNwd3eHuro6Dh8+jL59+3ItqdGTkpKCp0+fwsHBAfn5+VBWVoaSktze+paA13fqlaGrq9sgHDpDOh49egQHBwexQ/f29sa1a9eYQ+cB+/btw7hx45CWlgaBQMCemHBMYWEhvL29MWrUKPTp0wexsbHMofOERYsWwdPTEyKRCGpqarXm0GsbuTj1K1euwM3NDfb29nj58iUA4MCBA7h69ao8zDN4zKFDh9CtWzdER0dDT08PJ0+exKZNm1hUPg4hIvEk1ZkzZ+LWrVufjKDIqDsePnwIe3t77NixA1u3bsWff/4JPTapkFOKiorw5MkTAGWvP4eEhNT7DIQyq//vf/+LoUOHQk1NDbdu3RJnmcnOzsbq1atlFsjgJ+/evcPUqVPh5uaGvLw89OvXD7GxsbUaQZAhHQcOHECXLl3w/PlzKCgooG3btlxLavRkZ2ejV69eyMvLw40bNzB79mz2NJMHeHl5wcnJCaWlpdDX128QCXJkduq//PILdu7cid27d0skfnBwcEBMTIys5hk85Pbt27CxscHevXuhoKCA5cuX49KlSzVKaMCQH+/evQMAjB07FseOHav7REQCAaCiUvbJAADk5eWhuLgY2traOHToEKKjo9GtWzeuZTV6yvuKr68vDhw40KDCVMvs1BMSEtCvX78K5dra2sjKypLVPINHEBG2b9+Onj17IiEhAcbGxrh06RKWLVvWoDpFfeTMmTOwsLBAcnIyVFVVMWrUqLoX0a0bUFhY9slAUVERevXqhVWrVgEAhg8fLo7pweCO5cuXw9HREcXFxbCwsICtrS3XkuSKzDMBDA0N8ejRI7Rq1Uqi/OrVq7CwsJDVPIMnvH37FtOnT8eff/4JABgxYgQCAwOhr6/PrbBGTvkrNw4ODvD09GRj5zyAiCASiaCiooKFCxeyPBM8obyvjBo1CqampvV2IlyVyBqSbvXq1WRlZUU3btwgTU1NunLlCh08eJAMDAxo69atsprnHBYmlujq1atkYmJCAEhZWZk2b97MQr3ygKioKOrZsye9fv2aayllxMcTdetW9tlIef36NTk7O9NPP/3EtRTGB+zbt4+cnZ3lGxZZBmrTr8h8qeLr6wuRSIRBgwbh/fv36NevH4RCIRYsWIDZs2fLfNHB4I7S0lKsXbsWy5YtQ2lpKSwtLREUFMQiX/GEFi1awNjYGMXFxVxLKSM/H7h1q9EGn7ly5QomTpyI9+/fY9q0aVzLYXyAoaEhjI2NUVJSAhUVFa7l1C7yujooLCyke/fuUWRkJOXm5srLLOc01jv1lJQUGjhwIAEgAOTq6ko5OTlcy2r0PH/+nGbMmEHv37/nWkpFGmlCl5KSElq5ciUpKChQ37596fnz51xLYhDRtWvXyNfXl2sZlcLrO/VyVFRUYGVlJS9zDA45d+4cpkyZgoyMDKirq+P333/H5MmT2Ss4PCA7OxuXL19GUlISOnTowLWcRk9KSgrc3NwQHh6OpUuXYunSpQ13rLae8ejRI0RERODdu3fQ0NDgWk6dUeNfn7SPl/bs2VPTXTDqmKKiIixZsgS//fYbAKBLly44evQo2rVrx7Gyxk1hYSH++OMPeHp6omPHjoiPj2dvG/CAs2fPYsqUKVBRUcGlS5fQv39/riU1etLS0nD+/Hm4u7tj0qRJmDhxYqPrKzWO/a6goAAzMzN069YNnzNx4sSJGovjA40l9vuTJ08wfvx4cWIJLy8v/Prrr1BVVeVYGePGjRsYOHAgwsPDYWdnx7WcT5OZCVy8CAweDDRtyrWaWqW0tBS2trYwMjLC3r17G0TQkobA5s2bsX79ety/fx/a2tpcy/kktelXauzUPT09ceTIEZiZmYkji+nq6spVHB9oDE796NGj+Pbbb5GTk4OmTZtiz549GD16NNeyGj3h4eFwdHSEQCBARkYGcxw84PHjxygqKkKHDh3w+vVr6OnpsWEpjikuLkZkZCT69OkDkUiEzMxM3off5WVCl+3btyM1NRULFy7EqVOnYGJigrFjx+Lvv//+7J07gz+8f/8eM2fOxPjx45GTkyNOMMEcOvdERUVhwIABCAsLA4D64dBfvQI2biz7bIAQEaZMmQJfX18AgL6+PnPoPMDf3x/Dhg3DmzdvoKCgwHuHXtvILfXqs2fPsHfvXuzfvx8lJSW4d+9eg8gI1VDv1O/evYvx48cjPj4eAoEAS5YswbJly9gkH45JSUmBkZERgLLH7vUqcEkDTb367t07vH79GmZmZnj69Cn09PQa1LmgvlLeVwoLCxEfH1+vwu/y8k69giEFBQgEAhARSktL5WWWIWeICH/88Qfs7OwQHx+PFi1a4OLFi1i1ahVz6Bxz584dWFhY4OLFiwBQvxx6A+XOnTuwtbXFuHHjQEQwNzdnDp0H7NmzBx06dEBaWhqEQmG9cui1jUxOvbCwEEeOHMEXX3yBtm3b4u7du9i2bRuSk5MbxF16QyMrKwtjx47FrFmzUFBQgOHDh+P27dsYOHAg19IaNeXBYzp37oxt27ax/No8gIiwY8cO2NnZQUVFBXv37mWP2nlAeV9xcXHBtm3b0Lx5c44V8ZCavuDu4eFBTZs2JWtra9q8eTNlZGTI+Mo8P2kowWeuX79OZmZm4lCvGzZs4E3IxMbMo0ePyNzcnK5du8a1FNlpIMFn3r59Sy4uLgSAvv/+e34G+mmEnDp1iiwtLRuEr+Fl8JmdO3fC1NQUFhYWiIiIQERERKXbBQcH13QXVbJmzRoEBwfjwYMHUFNTQ+/evbFu3TqJ96oLCgowf/58BAUFobCwEEOHDsXvv//eaK7wRCIRfv31VyxZsgSlpaWwsLBAUFAQevTowbU0BgBTU1OMHDmy7tOk1gba2sDIkWWf9ZTr169jwoQJyM7ORnBwMMaMGcO1JMb/6N69O0aOHNmoAsnUhBo/fp88eTIGDBgAHR0daGtrf3KpTSIiIuDp6YkbN24gJCQExcXFGDJkiDhXLgDMnTsXp06dwvHjxxEREYGUlBR89dVXtaqLL7x69QrDhg2Dr68vSktLMX78eNy6dYs5dI7JyMjAyJEjkZCQAGVlZWzZsqVh5KJv3Ro4ebLssx6yfv169O3bF8bGxoiNjWUOnQfExMRgxIgRePfuHYyMjLBx40aoqalxLYvX1PhOfe/evXKUUTPOnz8v8f/evXvRrFkzREdHo1+/fsjOzkZAQAAOHz4sHjcODAxEhw4d6t/M4moSEhKCSZMm4dWrV1BTU4O/vz+mTZvGxgV5gLq6OvLz8/H27VuupciX4mIgKwvQ0QGUlblWU20KCgrg6+uL5cuXs0mjPEFNTQ35+fnIzs5md+jSIvcH+hySmJhIAOju3btERBQaGkoAKDMzU2I7U1NT2rhxY6U2CgoKKDs7W7w8f/68Xo2pFxUVka+vrzgRS+fOnenevXtcy2r0FBYW0k8//UQpKSlcS6k96uGY+t9//03+/v5cy2B8QFpaGi1atIiKi4u5llJrlI+pP3/+XMLfFBQUyGxbbq+0cY1IJIK3tzccHBzQqVMnAGVxgFVUVKCjoyOxbfPmzZGWllapnTVr1kgMH9Snsc6kpCT069cPa9euBQB4eHggMjKSJdrhAdnZ2Thw4ACuX7/OtRTGB1y+fBnnz5+HSCTiWgrjfzx69AgHDhzA48ePuZZS65iYmEj4mzVr1shss8E8Y/L09ERcXByuXr0qk53Fixdj3rx54v9zcnLqhWP/z3/+gxkzZiA7Oxva2toICAiAi4sL17IaPcHBwRg8eDAMDAzw4MEDFkufBzx9+hTXrl2Dq6srli9fDgUFBSgoNJj7m3pJSUkJjh49iokTJ8LBwQGPHz9uFH3l+fPnEnEPhEKhzDYbxC/Zy8sLp0+fRlhYmMSEI0NDQxQVFSErK0ti+1evXsHQ0LBSW0KhEFpaWhILn8nPz4eHhwe++eYbZGdnw97eHrGxscyh84CMjAy4u7vj0KFDANAoTlJ85/jx4+jatStWrlyJwsJCKCkpMYfOAyIiIuDu7o7Y2FgAjaevfOxreOHUjxw58sl1Pj4+spr/LEQELy8vnDhxApcuXYK5ubnEehsbGygrKyM0NFRclpCQgOTkZNjb29eqtrogPj4ednZ22LlzJwQCARYvXoyIiAi0atWKa2mNmvj4eBQVFcHAwAC3b9/GrFmzuJbU6Hn//j2+++47jB07FsOHD0dUVJRcTqAM2bh9+zYAYNCgQXj48CGLDCcPZB2U19bWprNnz1Yo9/b2JkNDQ1nNfxYPDw/S1tam8PBwSk1NFS8fBouYNWsWmZqa0qVLl+jmzZtkb29P9vb2Uu+Dj8FnRCIR/d///R+pqakRAGrevDlduHCBa1kMKgtcoqWlRevXr+daSt1TUkKUnV32ySPi4uKoY8eOpKamRrt37yaRSMS1JAYRRUREEAD6559/uJZS59SmX5HZqZ8+fZq0tbXpypUr4jIvLy8yMjKi+/fvy2r+s+B/M7w/XgIDA8Xb5Ofn0/fff09NmzYldXV1GjNmDKWmpkq9D7459aysLBo/frz4WIcMGUJpaWlcy2r0ZGVliSP0hYaGUn5+PseKGCKRiHbt2kVqamrUsWNHiouL41oSg0j8NpJIJKKzZ882yossXjt1IqJDhw5R06ZN6ebNm+Th4UFGRkaUkJAgD9OcwyenHhUVRRYWFgSAlJSUaN26dSzUKw/IycmhVq1a0ebNm7mWwi0PHxINGVL2yQMWLlxIAOi7775joV55QnR0NGlpadH169e5lsIpvAwT+yETJ05EVlYWHBwcYGBggIiICFhaWsrDNANlr+tt2rQJvr6+KCkpQatWrXDkyJEGHTynPkBEEAgE0NTUhK+vL4YNG8a1JG7JzQUuXCj75JDS0lIoKipi/PjxsLW1xTfffMOpHsb/7yvW1tb48ccf0blzZ64lNVhqlE/9w1e+PuT48ePo3r07Wn8QJnLjxo01V8cDuM6nnp6eDnd3d5w7dw4A8PXXX2P37t0V3r1n1C35+fkYNWoUpk6diokTJ3Ithx/wIJ96YGAgdu3ahfDwcDYRjic8evQILi4uCAoKQocOHbiWwwtq06/U6E791q1blZZbWloiJydHvJ6FJJWNS5cuwc3NDampqVBVVcWWLVswc+ZMVq88QFVVFZ07d/7kq5EMbujSpQv69+/PXlPjEUZGRujcuTO7yKor5P5Av4HBxZh6cXExLVmyhAQCAQEgKysrcehbBncUFxfTwoULKSQkhGsp/ISjMLEhISE0cuRIKiwsrNP9Mj5NRkYGubm50YsXL7iWwktq06+wy1mekZycjP79+8PPzw9EhJkzZ+Lff/8Vh75lcIeCggLi4uIaRfjKGmFiAmzbVvZZBxQXF+PHH3/EkCFDkJ+fj7y8vDrZL0M64uLikJSUxLWMRkeNxtQ/ZM2aNWjevDmmTZsmUb5nzx5kZGRg0aJFMgnkmrocU//zzz8xbdo0ZGZmQktLC7t27cK4ceNqdZ+Mqjl+/DjatGmDrl27iif8MLjl2bNnmDBhAqKiovDLL79g4cKF7JE7x5SUlGDLli2YMWMGtLW1WV/5DLXpV2TuBX/88Qfat29fobxjx47YuXOnrOYbBQUFBZg9ezbGjBmDzMxM2NnZ4datW8yh84CSkhKsXr1aHDmRnaQ+w9u3wMGDZZ+1SHBwMLp27YqUlBRcvnwZvr6+zKHzgNTUVKxevRqXLl0CwPoKZ8j6/F4oFNKTJ08qlD9+/JiEQqGs5jmntsfU79+/T126dBEHk/Hx8ZHb2KBIJKINGzZQq1atSE1NjZydnSkrK0sutqWloKCAPD09ycvLSy5pBeuKuLg4SkpKIqKyYBnyCpDBhzaRhhq1Wy2Pqb9//548PDwIALm4uNDbt2/lZrtBt0stExISItbycZprWWjIbcLr4DOWlpZ04MCBCuX79+8nc3NzWc1zTm1VvkgkosDAQFJXVycAZGBgQOfOnauWjWvXrhEA+vLLLytdP3/+fLK0tKSIiAi6efMmtWzZkry9veUhX4Jt27aRmZkZCYVCsrOzo8jISPG6PXv20IkTJ+ivv/6i3bt3y33ftUFpaSlZWVmRu7t7tb/LlzYpZ82aNQSA5syZIy5btmxZhSiM7dq1k/hejdqtlp36+vXrSSgU0o4dO6p9kcWHdsnJyaE5c+aQqakpqaqqkr29PUVFRUls87m+RMS//vTixQtSUVGpkRY+tMmH1GVf4bVTX7duHenp6dGePXsoKSmJkpKSKCAggPT09Gj16tXy0MgptVH5OTk55ObmJv6RDBw4kFJSUqptZ/r06TRhwgQSCoX08uVLiXU3btwggUBA0R+cYFesWEFt27aVWf+HBAUFkYqKCu3Zs4fu3btHM2fOJB0dHXr16hUREf3f//0f/fe//6W//vqLdu3aJdd9y5vMzEx68+YNERElJCTUKAoZH9qknKioKGrVqhVZW1tXOFF17NhRIl9CRkaGxHdr1G614NRFIhHFx8cTEVFhYaH47+rCh3YZO3YsWVlZUUREBCUmJtKyZctIS0tLPEO8qr5ExJ/+lJSUJI5mGRsbW6MnWXxok3Lquq/w2qmLRCJauHAhqaqqkoKCAikoKJC6ujqtWLFCHvo4R96Vf/PmTbK0tCQApKioSH5+flRSgwQYubm51KRJE4qMjKRhw4aRn5+fxPqvv/6aBg8eLFG2c+dO0tXVlUn/x9jZ2ZGnp6f4/9LSUjIyMqI1a9YQUVns/fJHU3yOhy4SicjW1pZcXV1rbIMvbVKupU2bNhQSEkKOjo4VTlRdunT57Pdr1G614NT3799PKioq4qGQmsCHdnn//j0pKirS6dOnJcq7d+9OS5YsIaKq+xIRP/pTeno6aWpq0u+//15jG3xokw+11HVf4bVTLyc3N5eioqLo7t27vBnrkQfyqnyRSESbNm0iZWVlAkCmpqZ09erVGtsLCAggS0tLIiI6cOAAtW7dWny1XFBQQGpqarR9+3aJ72zevLnSIRE/Pz/S0ND47PLs2bMK3yssLCRFRUU6ceKERPnkyZNp1KhRNT62uqS0tFT8ew0PD690foi08KFNypk8ebL4UWVlJyp1dXVq0aIFmZub08SJEz9rS2oePCDq1avsU0bKx8sLCgro5MmTMtniQ7vk5OQQALp48aJEuYODAzk6OtaLvvShwzp+/Djl5ubW2BYf2qQcLvpKvXDqDRV5VH5GRgaNGDFC/Lh9zJgx4se8NaV37960bNkyIiq7oFJXV6ewsDAi+v9jVaqqqhI/bBUVFRo6dGgFW2/evKHExMTPLsXFxRW+9/LlSwJA165dkyj38fEhOzs7mY6vLhCJRDRmzBj67rvv5GKPD21CRHTkyBHq1KmT+CT88Ynq7NmzdOzYMbp9+zadP3+e7O3tydTUlHJycuRSD7JQWlpKGzZsIE1NTbllVeNLu9jb25OjoyO9fPmSSkpK6MCBA6SgoEBt27blfV96+/YttW3blvbv3y8Xe3xpE676Cu8TuoSGhiI0NBTp6ekQiUQS6/bs2SOPXdRbIiIiMHHiRKSkpEAoFGLjxo3w8PCQ6XWPhIQEXLt2DXv37gUANGnSBM7OzggICED//v3x8OFDaGhoIDY2VuJ7Tk5OcHBwqGBPV1cXurq6NdZTXxEIBHBxcYGBgYHMtvjSJs+fP8ecOXMQEhICVVXVSrcZPny4+G9ra2v07NkTZmZmOHbsGKZPn17tfcqLjIwMuLu74+zZs5g/fz7atGkjs02+tAsAHDhwANOmTYOxsTEUFRXRvXt3TJgwAdHR0TWyV5c0bdoUkyZNQs+ePWW2xZc2qc995bPIelWwfPlyUlBQIDs7O3J2dqbRo0dLLPWdml5RlZSU0LJly0hBQUE8YzI2NlYumnx8fKhHjx4SZadPnyY1NTXKysqirVu3kpmZmcT6pKQkEggEdO/evQr2Gtvj96VLl8p9zgdf2uTEiRPi+RrlCwASCASkqKj4yfkbtra25OvrW/MKIJJpTP3SpUvUokUL0tfXpzNnzsim4wP40i4fkpeXJ54YO3bsWPryyy952Zfy8vJo9OjR9Pfff8vVLl/ahMu+wus79Z07d2Lv3r2YNGmSrKYaDC9evICrqysuX74MAJg6dSr8/f2hoaEhs+2SkhLs378fvr6+EuVDhgyBuro6jhw5An19fWRnZ0tEdPLz88OXX34JKyurCjZnzZqFsWPHfna/RkZGFcpUVFRgY2OD0NBQjB49GkBZmtjQ0FB4eXnV8AhrH1VVVSgpyeUhFQB+tcmgQYNw9+5dibKpU6eiffv2WLRoERQVFSt8Jy8vD48fP+akD5eUlGDlypX45Zdf0L9/fxw8eLDS46qpbb60y4doaGhAQ0MDmZmZ+Pvvv7F+/Xpe9iU1NTWoq6ujtLRUbjb51Cb1ra9IjaxXBbq6uvTo0SNZzfCW6l5RnTx5knR1dQkANWnShA4ePChXPeVXlyEhIXT37l2JZcyYMWRra0tpaWmkqqpKq1evpidPntCqVavI0NCQkpOT5aqFqOw1HKFQSHv37qX4+Hj69ttvSUdHh9LS0uS+L1n473//S4GBgbVim29t8jEfjxPOnz+fwsPD6enTp/TPP//Q4MGDSV9fn9LT02XbUTXv1JOTk6lv376koKBAq1atqtFbIJ+Db+1y/vx5OnfuHD158oQuXLhAXbp0oZ49e1JRURER8aMvlZSU0MqVK+nmzZu1Yp9vbfIxddVXeD1RbuHChbRy5Up5aOEl0lZ+QUEBzZkzRzwZrnv37pSYmCh3PR9OuPvUcvv2bQoKCiITExNSU1MjJyenWr3w8vf3J1NTU1JRUSE7Ozu6ceNGre2rpsyaNYvc3NxqxTYf2+RDPj5RjRs3jlq0aEEqKipkbGxM48aNk4+Wajr1a9eukYWFBV25ckX2fVcC39rl6NGjZGFhQSoqKmRoaEienp4VIqRx3ZcKCgrIzs6uwsxzecG3NvmYuuortenUZU7oMmfOHOzfvx/W1tawtraGsrKyxPqNGzfKYp5zpAm8n5iYiHHjxonzyM+dOxdr1qxh+YM55v79+3j58iUGDx6M4uJiKCkpsXjUtUlMDGBjA0RHA927V7pJQUEBdu7cCS8vLygpKaG4uLjCOYNR95w9exbt27eHhYUFa5M6oDYTusjs1AcMGPBp4wKBOLh/faWqyj948CA8PDyQl5cHPT097Nu3D05OThwoZXzMhAkT8OLFC1y+fJk587qgoAB48QJo2RL4xGziqKgoDBgwAKGhoejVq1cdC2RURlFREaysrPD1119j7dq1XMtpFPDaqTd0PlX5eXl58PLywr59+wAAjo6OOHToEIyNjbmSykBZe7148QJWVlbIysqCsrKyXCYoMmTj7NmzGDp0KBQVFfH69Wvo6+tzLanR8+jRI2hra8PAwACpqakwNDRkF791BK9Tr5YTHx+P8+fP4+TJk+Ll1KlT8jLPK2JjY2FjY4N9+/ZBQUEBK1asQGhoKHPoPMDDwwPjx4+HSCSCjo4Oc+h1ydOngJtb2ef/yMvLw5QpU+Dk5ISTJ08CAHPoPKCkpARDhw7F0qVLAQAtWrRgDr2hIOug/OPHj8na2poEAgEpKCiQQCAQ/62goCCreblRVfajT/HhhAaRSET+/v6koqJCAKhly5YUERFRy8oZVVFaWipOsvDs2bMG/TYGr/loolxMTAy1adOGmjRpUmkmR0bdk5eXR+/evSOisjwUtZVSmvF5anOinMx36nPmzIG5uTnS09Ohrq6Oe/fu4fLly7C1tUV4eLis5uXC0aNHMW/ePCxbtgwxMTHo0qULhg4divT0dKltvH37FmPGjMHs2bNRVFSEUaNGITY2Fv369atF5Qxp8Pb2xuDBg1FaWgpTU1O0bt2aa0mNGiLC1q1b0atXL2hqaiImJgZubm5cy2r0iEQiODo6YsGCBQAAGxsbuT/6ZfAAWa8K9PT06Pbt20REpKWlRQ/+l8whNDSUunbtKqt5uSBN9qNPUX5FZWxsTABIRUWFtmzZUqNUgwz5Uv5ec2xsrFyjkDFqSHQ0vQZolKMjASBvb+8GldypviISicR95cSJE3T//n2OFTF4HVGutLQUmpqaAMrGylJSUtCuXTuYmZkhISFBVvMyU1RUhOjoaCxevFhcpqCggMGDB+P69esVti8sLERhYaH4/+zsbADAy5cvYWFhgcDAQHTt2hW5ubm1L57xSbZv344LFy4gODgY5ubmMDc3R05ODteyGjUJ9+5hNID8W7cQFBSE4cOHV+hPjLqFiDB16lRYWlrip59+wsCBAwGA9RWOKa//cv9SjlAolP1VaFmvCvr06SOOVzxhwgQaNmwYXb16lSZPnkwdO3aU1bzMVDf70bJly6oMjsAWtrCFLWxhi7yX8sx1siDznfpPP/2Ed+/eAQBWrlyJESNGoG/fvtDT08PRo0dlNV/nLF68GPPmzRP/n5WVBTMzMyQnJ0NbW5tDZdKTk5MDExMTPH/+vN6MmTHNdUN91AzUT91Mc91QHzVnZ2fD1NQUz549g46OjrhcHgHLZHbqQ4cOFf9taWmJBw8e4O3bt2jatCkvXpHQ19eHoqIiXr16JVH+6tUrGBoaVtj+U48/tLW1680PphwtLS2muQ5gmuuO+qibaa4b6qNmHR0d/r6n/iG6urq8cOiAZCaxcsqzH9nb23OojMFgMBgM+VKjO/UPH09XBR9iv8+bNw9TpkyBra0t7OzssHnzZrx79w5Tp07lWhqDwWAwGHKjRk69PHFJVfDlbn3cuHHIyMjAzz//jLS0NHTt2hXnz59H8+bNq/yuUCjEsmXL6lVyFqa5bmCa6476qJtprhuYZklY7HcGg8FgMBoINR5TF4lEWLduHRwcHNCjRw/4+voiPz9fntoYDAaDwWBUgxo7dT8/P/z4449o0qQJjI2NsWXLFnh6espTG4PBYDAYjGpQ48fvbdq0wYIFC/Ddd98BAC5evAgnJyfk5+dDQaFWJtUzGAwGg8H4DDV26kKhEI8ePYKJiYm4TFVVFY8ePULLli3lJpDBYDAYDIZ01PiWuqSkBKqqqhJlysrKKC4ullkUn9i+fTtatWoFVVVV9OzZE1FRUVxLErNmzRr06NEDmpqaaNasGUaPHl0h3n5BQQE8PT2hp6eHJk2awMXFpUIgHq5Yu3YtBAIBvL29xWV81fvy5Uu4ublBT08Pampq6Ny5M27evCleT0T4+eef0aJFC6ipqWHw4MFITEzkTG9paSmWLl0Kc3NzqKmpoXXr1li1ahU+vIbnWvPly5cxcuRIGBkZQSAQ4M8//5RYL42+t2/fwtXVFVpaWtDR0cH06dORl5fHiebi4mIsWrQInTt3hoaGBoyMjDB58mSkpKTwVvPHzJo1CwKBAJs3b+a95vv372PUqFHQ1taGhoYGevTogeTkZPH6uj6XVKU5Ly8PXl5eaNmyJdTU1GBlZYWdO3dKbCMPzTV26kQEd3d3fPXVV+KloKAAs2bNkiirz8gjZWttEhERAU9PT9y4cQMhISEoLi7GkCFDxGF7AWDu3Lk4deoUjh8/joiICKSkpPCiXf7991/88ccfsLa2lijno97MzEw4ODhAWVkZ586dQ3x8PDZs2ICmTZuKt1m/fj22bt2KnTt3IjIyEhoaGhg6dCgKCgo40bxu3Trs2LED27Ztw/3797Fu3TqsX78e/v7+vNH87t07dOnSBdu3b690vTT6XF1dce/ePYSEhOD06dO4fPkyvv32W040v3//HjExMVi6dCliYmIQHByMhIQEjBo1SmI7Pmn+kBMnTuDGjRswMjKqsI5vmh8/fow+ffqgffv2CA8Px507d7B06VKJG826PpdUpXnevHk4f/48Dh48iPv378Pb2xteXl44efKkfDXXNGi8u7u7VEt9RpaUrVyQnp5OACgiIoKIiLKyskhZWZmOHz8u3ub+/fsEgK5fv86VTMrNzaU2bdpQSEgIOTo60pw5c4iIv3oXLVpEffr0+eR6kUhEhoaG9Ouvv4rLsrKySCgU0pEjR+pCYgWcnJxo2rRpEmVfffUVubq6EhH/NAMQJ4aSVl98fDwBoH///Ve8zblz50ggENDLly/rXHNlREVFEQB69uwZEfFX84sXL8jY2Jji4uLIzMyMNm3aJF7HR83jxo0jNze3T36H63NJZZo7duxIK1eulCjr3r07LVmyRK6aa3ynHhgYKNVSXylP2Tp48GBx2edStvKB8jR+urq6AIDo6GgUFxdLHEP79u1hamrK6TF4enrCyclJQhfAX70nT56Era0tvvnmGzRr1gzdunXD7t27xeufPn2KtLQ0Cd3a2tro2bMnZ7p79+6N0NBQPHz4EABw+/ZtXL16FcOHD+et5g+RRt/169eho6MDW1tb8TaDBw+GgoICIiMj61xzZWRnZ0MgEIiTdvBRs0gkwqRJk+Dj44OOHTtWWM83zSKRCGfOnEHbtm0xdOhQNGvWDD179pR43M3Hc0nv3r1x8uRJvHz5EkSEsLAwPHz4EEOGDJGrZjZN/RO8fv0apaWlFaLONW/eHGlpaRyp+jQikQje3t5wcHBAp06dAABpaWlQUVGRyAIEcHsMQUFBiImJwZo1ayqs46NeAHjy5Al27NiBNm3a4O+//4aHhwd++OEH7Nu3DwDE2vj0W/H19cX48ePRvn17KCsro1u3bvD29oarqysAfmr+EGn0paWloVmzZhLrlZSUoKury4tjKCgowKJFizBhwgRx0g4+al63bh2UlJTwww8/VLqeb5rT09ORl5eHtWvXYtiwYbhw4QLGjBmDr776ChEREWLNfDuX+Pv7w8rKCi1btoSKigqGDRuG7du3o1+/fgDkp1nmLG0MfuDp6Ym4uDhcvXqVaymf5Pnz55gzZw5CQkIqTLLkMyKRCLa2tli9ejUAoFu3boiLi8POnTsxZcoUjtVVzrFjx3Do0CEcPnwYHTt2RGxsLLy9vWFkZMRbzQ2J4uJijB07FkSEHTt2cC3nk0RHR2PLli2IiYnhTVjvqhCJRAAAZ2dnzJ07FwDQtWtXXLt2DTt37oSjoyOX8j6Jv78/bty4gZMnT8LMzAyXL1+Gp6cnjIyMKjy1lAV2p/4JqpuylUu8vLxw+vRphIWFSbxOaGhoiKKiImRlZUlsz9UxREdHIz09Hd27d4eSkhKUlJQQERGBrVu3QklJCc2bN+eV3nJatGgBKysribIOHTqIZ9qWa+PTb8XHx0d8t965c2dMmjQJc+fOFT8h4aPmD5FGn6GhYYVJqyUlJXj79i2nx1Du0J89e4aQkBCJ1Jp803zlyhWkp6fD1NRU3CefPXuG+fPno1WrVrzUrK+vDyUlpSr7JJ/OJfn5+fjxxx+xceNGjBw5EtbW1vDy8sK4cePw22+/yVUzc+qfoD6kbCUieHl54cSJE7h06RLMzc0l1tvY2EBZWVniGBISEpCcnMzJMQwaNAh3795FbGyseLG1tYWrq6v4bz7pLcfBwaHCq4IPHz6EmZkZAMDc3ByGhoYSunNychAZGcmZ7vfv31cIAqWoqCi+y+Gj5g+RRp+9vT2ysrIQHR0t3ubSpUsQiUTo2bNnnWsG/r9DT0xMxMWLF6Gnpyexnm+aJ02ahDt37kj0SSMjI/j4+ODvv//mpWYVFRX06NHjs32Sb+e+4uJiFBcXf7ZPyk1zDSf3NQqCgoJIKBTS3r17KT4+nr799lvS0dGhtLQ0rqUREZGHhwdpa2tTeHg4paamipf379+Lt5k1axaZmprSpUuX6ObNm2Rvb0/29vYcqpbkw9nvRPzUGxUVRUpKSuTn50eJiYl06NAhUldXp4MHD4q3Wbt2Leno6NBff/1Fd+7cIWdnZzI3N6f8/HxONE+ZMoWMjY3p9OnT9PTpUwoODiZ9fX1auHAhbzTn5ubSrVu36NatWwSANm7cSLdu3RLPFJdG37Bhw6hbt24UGRlJV69epTZt2tCECRM40VxUVESjRo2ili1bUmxsrESfLCws5KXmyvh49jsfNQcHB5OysjLt2rWLEhMTyd/fnxQVFenKlStiG3V9LqlKs6OjI3Xs2JHCwsLoyZMnFBgYSKqqqvT777/LVTNz6lXg7+9PpqampKKiQnZ2dnTjxg2uJYkBUOkSGBgo3iY/P5++//57atq0Kamrq9OYMWMoNTWVO9Ef8bFT56veU6dOUadOnUgoFFL79u1p165dEutFIhEtXbqUmjdvTkKhkAYNGkQJCQkcqSXKycmhOXPmkKmpKamqqpKFhQUtWbJEwrlwrTksLKzS3++UKVOk1vfmzRuaMGECNWnShLS0tGjq1KmUm5vLieanT59+sk+GhYXxUnNlVObU+ag5ICCALC0tSVVVlbp06UJ//vmnhI26PpdUpTk1NZXc3d3JyMiIVFVVqV27drRhwwYSiURy1cxSrzIYDAaD0UBgY+oMBoPBYDQQmFNnMBgMBqOBwJw6g8FgMBgNBObUGQwGg8FoIDCnzmAwGAxGA4E5dQaDwWAwGgjMqTMYDAaD0UBgTp3BYDAYjAYCc+oMBoPBYDQQWOpVBoPB4DHVSYnKAoQymFNnMBgMHsMcNaM6sMfvDAaDwWA0EJhTZzAYn6V///7w9vbmjX1ptq9tzdVFHnqICBs3boS5uTnU1dUxevRoZGdn19jemzdv0KxZMyQlJX12O77VZVWMHz8eGzZs4FoGZzCnXs9xd3fH6NGjK5SHh4dDIBAgKytLovz69etQVFSEk5OTRPnOnTuhqamJkpIScVleXh6UlZXRv3//Sm0/fvxYXJaWlobZs2fDwsICQqEQJiYmGDlyJEJDQ2U+xnLc3d0hEAgwa9asCus8PT0hEAjg7u4ut/3VlPqiszIqO4EHBwdj1apVNbZZXh8fL48ePZKLfVn4VH/gIz4+PtixYwf27duHK1euIDo6GsuXL6+xPT8/Pzg7O6NVq1Zy08gHfvrpJ/j5+cl0wVOfYU69kREQEIDZs2fj8uXLSElJEZcPGDAAeXl5uHnzprjsypUrMDQ0RGRkJAoKCsTlYWFhMDU1RevWrQEASUlJsLGxwaVLl/Drr7/i7t27OH/+PAYMGABPT89Paunfvz/27t1bLf0mJiYICgpCfn6+uKygoACHDx+GqalptWzVJvVFpzTo6upCU1NTJhvDhg1DamqqxGJubi43+zXlU/2Bb0RGRmLjxo04evQo+vXrBxsbG8ycORNnz56tkb33798jICAA06dPl7PSmlFUVCQ3W506dULr1q1x8OBBudmsTzCn3ojIy8vD0aNH4eHhAScnJwmH2q5dO7Ro0QLh4eHisvDwcDg7O8Pc3Bw3btyQKB8wYID4/++//x4CgQBRUVFwcXFB27Zt0bFjR8ybN0/ie/Kge/fuMDExQXBwsLgsODgYpqam6Natm8S2IpEIa9asgbm5OdTU1NClSxf85z//Ea8/f/48+vTpAx0dHejp6WHEiBESTx+AsguPH374AQsXLoSuri4MDQ2lujuqjk5pdOTm5sLV1RUaGhpo0aIFNm3aJHFXLY3OqurD3d0dERER2LJli/huOikpqcLdu0gkwvr162FpaQmhUAhTU1P4+fl9tj6EQiEMDQ0lFkVFRbH2j+1/TufHvHv3DpMnT0aTJk3QokULqR+9fq4/lCNNvVbVNh9T3eMDgN9++w2DBg1C9+7dxWXNmzfH69evpTrWjzl79iyEQiF69eolUS5NXValX5r66N+/P7y8vODt7Q19fX0MHTpUKtvS1t3IkSMRFBRUo7qp7zCn3og4duwY2rdvj3bt2sHNzQ179uyRmFk7YMAAhIWFif8PCwtD//794ejoKC7Pz89HZGSk2Km/ffsW58+fh6enJzQ0NCrsU0dHR+7HMW3aNAQGBor/37NnD6ZOnVphuzVr1mD//v3YuXMn7t27h7lz58LNzQ0REREAyk5g8+bNw82bNxEaGgoFBQWMGTMGIpFIws6+ffugoaGByMhIrF+/HitXrkRISIjcdEqjY968efjnn39w8uRJhISE4MqVK4iJiamWzqrqY8uWLbC3t8fMmTPFd9MmJiYV9C5evBhr167F0qVLER8fj8OHD6N58+ZV1oe0VKXzY3x8fBAREYG//voLFy5cQHh4eIW6qYyq+kM5VdWrNG0jy/EVFhbizJkzGDNmjER5QUEBtLW1qzzOyrhy5QpsbGwqlEtTl1Xpl7Y+9u3bBxUVFfzzzz/YuXOnVLalrTs7OztERUWhsLCwRvVTryFGvWbKlCmkqKhIGhoaEouqqioBoMzMTPG2vXv3ps2bNxMRUXFxMenr61NYWJh4/e7du0lDQ4OKi4spJyeHlJSUKD09nQ4fPkz9+vUjIqLQ0FACQM+ePSMiosjISAJAwcHB1dbu6OhIgYGB1TpWZ2dnSk9PJ6FQSElJSZSUlESqqqqUkZFBzs7ONGXKFCIiKigoIHV1dbp27ZqEjenTp9OECRMqtZ+RkUEA6O7duxIa+/TpI7Fdjx49aNGiRXLRKY2OnJwcUlZWpuPHj4u3ycrKInV1dZozZ45UOqWtD0dHR7HNyspycnJIKBTS7t27P6m/svr4+Df69ddfV2pfGp0fbp+bm0sqKip07Ngx8bZv3rwhNTW1CsfxMVX1h/J9fa5epWmbDzXX5Hd57do1AkCqqqoSdaiiokJDhw4Vb7dhwwYyNjamLl26UKtWrej777//5LE7OzvTtGnTJMqkqcuq9FenPrp16yZhoyrb1am727dvEwBKSkr6ZB00VNh76g2AAQMGYMeOHRJlkZGRcHNzE/+fkJCAqKgonDhxAgCgpKSEcePGISAgQDwRrn///nj37h3+/fdfZGZmom3btjAwMICjoyOmTp2KgoIChIeHw8LCQjwuTNV4h3b16tVYvXq1+P/8/HzcuHEDXl5e4rL4+Pgqx5wNDAzEj0uJCE5OTtDX15fY5tGjR3j//j2++OILifKioiLx4+/ExET8/PPPiIyMxOvXr8V3xsnJyejUqZP4O9bW1hI2WrRogfT09CqPVxqd0uh48uQJiouLYWdnJ/6OtrY22rVrJ2HnczqlqQ9puH//PgoLCzFo0CCpvwNU/I1W9lSnJjofP36MoqIi9OzZU1ymq6tboW4+Rpr+UM7n6lXatqnp8QHAw4cPoaGhgdjYWIlyJycnODg4iP+Pi4vDli1b4OLigvz8fOjr62PdunVo0qRJBZv5+flQVVWVKJOmLqvSX536+PhJQVW2q1N3ampqAMrmDjQ2mFNvAGhoaMDS0lKi7MWLFxL/BwQEoKSkBEZGRuIyIoJQKMS2bdugra0NS0tLtGzZEmFhYcjMzISjoyMAwMjICCYmJrh27RrCwsIwcOBAsY02bdpAIBDgwYMHVeqcNWsWxo4dK/7f1dUVLi4u+Oqrr8RlH+r7HNOmTRNfDGzfvr3C+ry8PADAmTNnYGxsLLFOKBQCKBt3MzMzw+7du2FkZASRSIROnTpVmLSjrKws8b9AIKjwiL6mOqujoyo+p1Oa+pCG8pNldansN1oZ8tJZFdL0h3Jkaf+Pqcnx5eTkQF9fX6L+nj17hsTERLi4uIjL4uLisGTJEgBAbGwsWrduXalDBwB9fX1kZmbKXf/bt2+ltvXxhV1VtssnMkpTd+U6DAwMpNbTUGBOvRFQUlKC/fv3Y8OGDRgyZIjEutGjR+PIkSPi168GDBiA8PBwZGZmwsfHR7xdv379cO7cOURFRcHDw0Ncrquri6FDh2L79u344YcfKnTUrKws8bi6rq4udHV1xevU1NTQrFkzqU72HzNs2DAUFRVBIBCIJ9l8iJWVFYRCIZKTk8UXJx/y5s0bJCQkYPfu3ejbty8A4OrVq9XWIatOaXRYWFhAWVkZ//77r/gpRnZ2Nh4+fIh+/fpJpaOq+ihHRUUFpaWln1zfpk0bqKmpITQ0FDNmzJBq39VBWp3ltG7dGsrKyoiMjBTXTWZmJh4+fPjJ71enP1RFddumuscHlDng7OxsEJE4ZKyfnx++/PJLWFlZASi7IElISICLiwvev3+PrKysz/6eu3XrVmF2uDR1WZV+HR2dGv9WpbEtbd3FxcWhZcuWlT4Za+gwp94IOH36NDIzMzF9+vQKE2tcXFwQEBAg4dQ9PT1RXFws0XEcHR3h5eWFoqIiiZnvQNkdqIODA+zs7LBy5UpYW1ujpKQEISEh2LFjB+7fvy/3Y1JUVBTbLZ9F/SGamppYsGAB5s6dC5FIhD59+iA7Oxv//PMPtLS0MGnSJOjp6WHXrl1o0aIFkpOT4evrW+c6mzZtWqUOTU1NTJkyBT4+PtDV1UWzZs2wbNkyKCgoSB0XvKr6mDJlCgCgVatWiIyMRFJSEpo0aSJxEQYAqqqqWLRoERYuXAgVFRU4ODggIyMD9+7dk8vrUdLqLKdJkyaYPn06fHx8oKenh2bNmmHJkiVQUPj0HODq9Adp9Fanbap7fAAwcOBAFBQUYO3atRg/fjwOHTqEU6dOISoqSrzN06dP0aFDB/HbJr/88gs2bdpUYViunKFDh2Lx4sXIzMxE06ZNAUhXl9Lor+lvVRrb0tbdlStXKlywNRaYU28EBAQEYPDgwZXOlHVxccH69etx584dWFtbY8CAAcjPz0f79u0lZjQ7OjoiNzdX/Orbh1hYWCAmJgZ+fn6YP38+UlNTYWBgABsbm0+eVOSBlpbWZ9evWrUKBgYGWLNmDZ48eQIdHR10794dP/74IxQUFBAUFIQffvgBnTp1Qrt27bB169YK46m1rVNaHRs3bsSsWbMwYsQIaGlpYeHChXj+/HmFcdHP8bn6KGfBggWYMmUKrKyskJ+fj6dPn1aws3TpUigpKeHnn39GSkoKWrRoIbUTlJfOD/n111+Rl5eHkSNHQlNTE/Pnz/9s4JHq9AdpqG7bVPf4mjdvjr1798LHxwerVq3CwIEDcfXqVYk3E+Li4iTGrTt37vzZGfidO3dG9+7dcezYMXz33Xficmnqsir9svxWq7ItTd0VFBTgzz//xPnz56vcX0NEQNWZ6cRgMHjBu3fvYGxsjA0bNvAmgAijDC7apnwC6o8//ojS0lK4urqib9++nw3+dObMGfj4+CAuLu6zTzZkpa7rY8eOHThx4gQuXLhQ6/viI+xOncGoB9y6dQsPHjyAnZ0dsrOzsXLlSgCAs7Mzx8oYfGibuLg4XL16FcePH4dAIMDw4cMl5r5UhpOTExITE/Hy5ctK4xHUFK7rQ1lZGf7+/nWyLz7CnDqDUU/47bffkJCQABUVFdjY2ODKlSuNciIQH+G6bQ4fPlyj79VWohYu66M2JnDWJ9jjdwaDwWAwGggsTCyDwWAwGA0E5tQZDAaDwWggMKfOYDAYDEYDgTl1BoPBYDAaCMypMxgMBoPRQGBOncFgMBiMBgJz6gwGg8FgNBCYU2cwGAwGo4HAnDqDwWAwGA0E5tQZDAaDwWggMKfOYDAYDEYDgTl1BoPBYDAaCP8PXRejuqrv1yoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 512x384 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the comparison on a 0 to 180 degrees plot (Planck vs HAWC+)\n",
    "FigSize=[5.12,3.84]\n",
    "PvH_plot = plt.figure(figsize=(FigSize[0],FigSize[1]))\n",
    "plt.xlabel(r'HAWC+ Mean Magnetic Field Angle $\\overline{\\theta}_B$ (degree)')\n",
    "plt.ylabel(r'Planck Mean Magnetic Field Angle $\\overline{\\theta}_{PB}$ (degree)')\n",
    "axes = plt.gca() # Calling the axes object of the figure\n",
    "plt.errorbar(Theta_B_180, Theta_PlaB_180, xerr=Uncertain_B, yerr=Uncertain_PlaB,\n",
    "             ecolor='gray',linestyle='None',alpha=0.6)\n",
    "plt.plot(Theta_B_180, Theta_PlaB_180, '.', color='black', markersize=8)\n",
    "axes.xaxis.set_ticks_position('both') # Adding ticks to each side\n",
    "axes.yaxis.set_ticks_position('both') # Adding ticks to each side\n",
    "plt.xlim(0.0,180.0)\n",
    "plt.ylim(0.0,180.0)\n",
    "plt.tight_layout() # Using all available space in the plot window\n",
    "# Creating disk lines\n",
    "plt.axvline(x=90.0, color='red', linestyle='--', linewidth=1)\n",
    "plt.axhline(y=90.0, color='red', linestyle='--', linewidth=1)\n",
    "# Creating zero difference line\n",
    "RefX = np.arange(0.0, 180.0, 0.1)\n",
    "RefY = np.arange(0.0, 180.0, 0.1)\n",
    "plt.plot(RefX, RefY, 'black')\n",
    "# Creating difference lines\n",
    "Ref45Xa = np.arange(0.0, 135, 0.1)\n",
    "Ref45Ya = np.arange(45.0, 180.0, 0.1)\n",
    "plt.plot(Ref45Xa, Ref45Ya, 'black', linestyle=':', linewidth=1)\n",
    "Ref45Yb = np.arange(0.0, 135, 0.1)\n",
    "Ref45Xb = np.arange(45.0, 180.0, 0.1)\n",
    "plt.plot(Ref45Xb, Ref45Yb, 'black', linestyle=':', linewidth=1)\n",
    "Ref45Xc = np.arange(0.0, 45.0, 0.1)\n",
    "Ref45Yc = np.arange(135.0, 180.0, 0.1)\n",
    "plt.plot(Ref45Xc, Ref45Yc, 'black', linestyle=':', linewidth=1)\n",
    "Ref45Yd = np.arange(0.0, 45.0, 0.1)\n",
    "Ref45Xd = np.arange(135.0, 180.0, 0.1)\n",
    "plt.plot(Ref45Xd, Ref45Yd, 'black', linestyle=':', linewidth=1)\n",
    "Ref90Xa = np.arange(0.0, 90.0, 0.1)\n",
    "Ref90Ya = np.arange(90.0, 180.0, 0.1)\n",
    "plt.plot(Ref90Xa, Ref90Ya, 'black', linestyle='-.', linewidth=1)\n",
    "Ref90Yb = np.arange(0.0, 90.0, 0.1)\n",
    "Ref90Xb = np.arange(90.0, 180.0, 0.1)\n",
    "plt.plot(Ref90Xb, Ref90Yb, 'black', linestyle='-.', linewidth=1)\n",
    "# Adding the labels to the difference lines\n",
    "plt.text(15, 5, r'$\\Delta \\theta = 0^{\\circ}$')\n",
    "plt.text(60, 5, r'$\\Delta \\theta = 45^{\\circ}$')\n",
    "plt.text(105, 5, r'$\\Delta \\theta = 90^{\\circ}$')\n",
    "plt.text(150, 5, r'$\\Delta \\theta = 45^{\\circ}$')\n",
    "plt.text(10, 170, r'$\\Delta \\theta = 45^{\\circ}$')\n",
    "plt.text(55, 170, r'$\\Delta \\theta = 90^{\\circ}$')\n",
    "plt.text(100, 170, r'$\\Delta \\theta = 45^{\\circ}$')\n",
    "plt.text(145, 170, r'$\\Delta \\theta = 0^{\\circ}$')\n",
    "# Creating label positions for each filament\n",
    "FilNames = ['Fil1', 'Fil2', 'Fil4', 'Fil5', 'Fil6', 'Fil8', 'Fil10', 'G24', 'G47', 'G49']\n",
    "NameShiftX = [-8,-8.5,6,9,9,-9,9,8,-8,-8]\n",
    "NameShiftY = [-12,-16,9,2,-10,-12,5,5,2,-9]\n",
    "FilLabelX = Theta_B_180 + NameShiftX\n",
    "FilLabelY = Theta_PlaB_180 + NameShiftY\n",
    "# Adding the labels to the points\n",
    "for i in range(0,len(FilNames)):\n",
    "    plt.text(FilLabelX[i], FilLabelY[i], FilNames[i], horizontalalignment='center')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving figure\n",
    "PvH_plot.savefig('All/Planck_Hawc_Mean_Comparison.png',dpi=300)\n",
    "PvH_plot.savefig('All/Planck_Hawc_Mean_Comparison.pdf',dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PoLiteWIP",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
